Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, existing approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning. ToolACE-R features a model-aware iterative training procedure that progressively adjust training samples based on the model’s evolving capabilities to maximize its potential. Additionally, it incorporates self-refinement training corpus which emphasizes LLM's ability to iteratively refine their tool calls, optimizing performance without requiring external feedback. Furthermore, we introduce adaptive self-refinement for efficient test-time scaling, where the trained model can autonomously determine when to stop the process based on iterative self-refinement. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced LLMs. The performance can be further improved efficiently through adaptive self-refinement. These results highlight the effectiveness and generalizability of ToolACE-R, offering a promising direction for more efficient and scalable tool learning.

Zhiyuan Yao, Zishan Xu, Yifu Guo, Zhiguang Han, Cheng Yang, Shuo Zhang, Weinan Zhang, Xingshan Zeng, Weiwen Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information. We present SeUL, a novel method that enables selective and fine-grained unlearning for language models. Unlike previous work that employs a fully reversed training objective in unlearning, SeUL minimizes the negative impact on the capability of language models, particularly in terms of generation. Furthermore, we introduce two innovative evaluation metrics, sensitive extraction likelihood (S-EL) and sensitive memorization accuracy (S-MA), specifically designed to assess the effectiveness of forgetting sensitive information. In support of the unlearning framework, we propose efficient automatic online and offline sensitive span annotation methods. The online selection method, based on language probability scores, ensures computational efficiency, while the offline annotation involves a two-stage LLM-based process for robust verification. In summary, this paper contributes a novel selective unlearning method (SeUL), introduces specialized evaluation metrics (S-EL and S-MA) for assessing sensitive information forgetting, and proposes automatic online and offline sensitive span annotation methods to support the overall unlearning framework and evaluation.

Zezhong Wang, Xingshan Zeng, Weiwen Liu, Yufei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data. However, the generation of the winning response and the losing response within pairwise data are typically isolated, leading to weak correlations between them as well as suboptimal alignment performance. To address this issue, we propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. Firstly, we increase the consistency and informativeness of the pairwise preference signals through targeted modifications, synthesizing a pseudo-winning response by improving the losing response with the winning response as a reference. Secondly, we identify that DPO alone is insufficient to model these correlations and capture nuanced variations. Therefore, we propose learning token-level correlations by dynamically leveraging the policy model's confidence during training. Comprehensive experiments on QA, math, and instruction-following tasks demonstrate the effectiveness of our approach, significantly surpassing competitive baselines, including DPO. Additionally, our in-depth quantitative analysis reveals the reasons behind our method's superior performance over DPO and showcases its versatility to other DPO variants.

Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pipelines often lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data, specifically tailored to the capabilities of LLMs. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, under the guidance of a complexity evaluator. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. We demonstrate that models trained on our synthesized data---even with only 8B parameters---achieve state-of-the-art performance, comparable to the latest GPT-4 models. Our model and a subset of the data are publicly available at https://huggingface.co/Team-ACE.

Conversational Recommender Systems (CRS) have become increasingly important due to their ability to recommend items through interactive dialogue, adapting to user preferences in real time. Traditional CRS approaches face challenges in generating high-quality, diverse responses due to the limited availability of training data and the inherited biases from domain-specific fine-tuning. Furthermore, existing systems often overlook the impact of confounding variables during user interactions, leading to suboptimal recommendations. In this work, we propose a novel hybrid framework that integrates large language models (LLMs) with traditional recommendation techniques to address these limitations. Our approach leverages the strengths of LLMs in generating fluent, contextually appropriate responses while employing a traditional recommendation module to capture complex interaction structures. To ensure unbiased recommendations, we introduce causal interventions that disentangle confounding variables, improving recommendation accuracy. We evaluate our framework on established CRS datasets, demonstrating significant improvements in recommendation quality and response generation. Our results highlight the effectiveness of the causal intervention mechanism in producing more reliable and personalized recommendations, while the LLM-based response generation offers scalability across multiple domains.

Zezhong Wang, Xingshan Zeng, Weiwen Liu, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Wai-Chung Kwan, Xingshan Zeng, Yufei Wang, Yusen Sun, Liangyou Li, Yuxin Jiang, Lifeng Shang, Qun Liu, Kam-Fai Wong. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, Yufei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of theexternal domain-specificinformation needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract theinternalknowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.Previous work mainly focuses on computer vision scenarios and largely ignores the essentials of unlearning in NLP field, where text data contains more explicit and sensitive personal information than images.In this paper, we propose a general unlearning framework called KGA to induce forgetfulness.Different from previous work that tries to recover gradients or forces models to perform close to one specific distribution, KGA maintains distribution differences (i.e., knowledge gap).This relaxes the distribution assumption.Furthermore, we first apply the unlearning method to various NLP tasks (i.e., classification, translation, response generation) and propose several unlearning evaluation metrics with pertinence.Experiments on large-scale datasets show that KGA yields comprehensive improvements over baselines, where extensive analyses further validate the effectiveness of KGA and provide insight into unlearning for NLP tasks 1 .

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for humans. This work studies automatic quotation recommendation for online conversations. Unlike the previous works that only consider semantic-level modeling, we adopt topic-level representation to facilitate the recommendation. A hierarchical architecture that is based on a pretrained language model is adopted to model the semantic-level conversation representation, and a neural topic model is employed to learn the topic-level representation. Moreover, the semantic-level conversation modeling is enhanced by a topic-aware attention mechanism, which is adopted to capture the interactive conversation structure from the perspective of word co-occurrence. The joint training of semantic- and topic-based recommendation leads to significantly better performance than the state-of-the-art models on two large-scale datasets. Apart from the novel and advanced recommendation framework, we conduct extensive quantitative experiments to investigate the difficulty of the quotation recommendation task, validate the topic-based recommendation assumption, and explore the stability of the recommendation. Some qualitative experiments and analyses are also included to interpret the quotation and topic distribution for some instances. All the extensive experiments and analyses provide persuasive explanations and interpretations of the module design and the recommendation results.

Most of the research to date focuses on bilingual sign language translation (BSLT). However, such models are in-efficient in building multilingual sign language translation systems. To solve this problem, we introduce the multilin-gual sign language translation (MSLT) task. It aims to use a single model to complete the translation between multiple sign languages and spoken languages. Then, we propose MLSLT, the first MSLT model, which contains two novel dy-namic routing mechanisms for controlling the degree ofpa-rameter sharing between different languages. Intra-layer language-specific routing controls the proportion of data flowing through shared parameters and language-specific parameters from the token level through a soft gate within the layer, and inter-layer language-specific routing controls and learns the data flow path of different languages at the language level through a soft gate between layers. In order to evaluate the performance of MLSLT, we collect the first publicly available multilingual sign language understanding dataset, Spreadthesign-Ten (SP-10), which contains up to 100 language pairs, e.g., CSL→en, GSG→zh. Experi-mental results show that the average performance of ML-SLT outperforms the baseline MSLT model and the com-bination of multiple BSLT models in many cases. In ad-dition, we also explore zero-shot translation in sign language and find that our model can achieve comparable performance to the supervised BSLT model on some language pairs. Dataset and more details are at https://mlslt.github.io/.

One of the challenging factors in real application of sign language translation (SLT) is inter-signer variation. With the assumption that the pre-trained translation model cannot cover all the signers, the adaptation capability for unseen signers is of great concern. In this paper, we take a completely different perspective for SLT, called signer-adaptive SLT, which mainly considers the transferable ability of SLT systems. To attack this challenging problem, we propose MC-SLT, a novel meta-learning framework that could exploit additional new-signer data via a support set, and output a signer-adaptive model via a few-gradient-step update. Considering the various degrees of style discrepancies of different words performed by multiple signers, we further devise diversity-aware meta-adaptive weights for the token-wise cross-entropy losses. Besides, to improve the training robustness, we adopt the self-guided curriculum learning scheme that first captures the global curricula from each signer to avoid falling into a bad local optimum early, and then learns the curricula of individualities to improve the model adaptability for learning signer-specific knowledge. We re-construct the existing standard datasets of SLT for the signer-adaptive setting and establish a new benchmark for subsequent research.

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people’s everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer’s chatting history and conversation’s ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others’ discussions.

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Lip reading, aiming to recognize spoken sentences according to the given video of lip movements without relying on the audio stream, has attracted great interest due to its application in many scenarios. Although prior works that explore lip reading have obtained salient achievements, they are all trained in a non-simultaneous manner where the predictions are generated requiring access to the full video. To breakthrough this constraint, we study the task of simultaneous lip reading and devise SimulLR, a simultaneous lip Reading transducer with attention-guided adaptive memory from three aspects: (1) To address the challenge of monotonic alignments while considering the syntactic structure of the generated sentences under simultaneous setting, we build a transducer-based model and design several effective training strategies including CTC pre-training, model warm-up and curriculum learning to promote the training of the lip reading transducer. (2) To learn better spatio-temporal representations for simultaneous encoder, we construct a truncated 3D convolution and time-restricted self-attention layer to perform the frame-to-frame interaction within a video segment containing fixed number of frames. (3) The history information is always limited due to the storage in real-time scenarios, especially for massive video data. Therefore, we devise a novel attention-guided adaptive memory to organize semantic information of history segments and enhance the visual representations with acceptable computation-aware latency. The experiments show that the SimulLR achieves the translation speedup 9.10x compared with the state-of-the-art non-simultaneous methods, and also obtains competitive results, which indicates the effectiveness of our proposed methods.

Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years. However, the existing sign language translation methods need to read all the videos before starting the translation, which leads to a high inference latency and also limits their application in real-life scenarios. To solve this problem, we propose SimulSLT, the first end-to-end simultaneous sign language translation model, which can translate sign language videos into target text concurrently. SimulSLT is composed of a text decoder, a boundary predictor, and a masked encoder. We 1) use the wait-k strategy for simultaneous translation. 2) design a novel boundary predictor based on the integrate-and-fire module to output the gloss boundary, which is used to model the correspondence between the sign language video and the gloss. 3) propose an innovative re-encode method to help the model obtain more abundant contextual information, which allows the existing video features to interact fully. The experimental results conducted on the RWTH-PHOENIX-Weather 2014T dataset show that SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model while maintaining low latency, which proves the effectiveness of our method.

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.In this research we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests.Different from works in conversation recommendation which assume static user interests, our model captures the temporal aspects of user interests.Moreover, our model can cater for cold start problem where conversations are new and unseen in training.We propose a neural architecture to analyze changes of user interactions and interests over time, whose result is used to predict which discussions the users are likely to enter.We conduct experiments on large-scale collections of Reddit conversations.Results on three subreddits show that our model significantly outperforms state-of-the-art models based on static assumption of user interests.We further evaluate performance in cold start, and observe consistently better performance by our model when considering various degrees of sparsity of user's chatting history and conversation contexts.Lastly, our analysis also confirms the change of user interests.This further justify the advantage and efficacy of our model.

Quotations are crucial for successful explanations and persuasions in interpersonal communications.However, finding what to quote in a conversation is challenging for both humans and machines.This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context.Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content.Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation.Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models.Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.

As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change.In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in.We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior.We experiment with two large-scale datasets collected from Twitter and Reddit.Results show that our proposed framework with biattention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-ofthe-art methods from previous work. * Jing Li is the corresponding author.…… H 1 : Is there literally no one on twitter who wants to talk about LET ME IN with me? :( H 2 : I think the change in overall tone was enough to let LMI stand on it's own.Love Giacchino's score too.

Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.