Large language models excel at problemsolving but often struggle with complex reasoning and factual accuracy.While chainof-thought and retrieval-augmented generation help break down problems and retrieve knowledge, they still falter on challenging tasks like competitive programming due to frequent reasoning errors and irrelevant retrieval.To address this, we introduce Critic-guided planning with Retrieval-augmentation, CR-Planner, a novel framework that leverages fine-tuned critic models to guide both reasoning and retrieval processes through planning.CR-Planner iteratively selects and executes sub-goals, guided by critic models.A sub-goal critic identifies promising sub-goals from reasoning, query generation, and retrieval, while an execution critic evaluates outputs of sub-goal executions.We employ Monte Carlo Tree Search to collect data for critic training, allowing systematic exploration of action sequences and effective navigation toward the final answer.We evaluate CR-Planner on challenging domain-knowledgeintensive and reasoning-heavy tasks, including competitive programming, theorem-driven math reasoning, and complex domain retrieval problems.It significantly outperforms baselines, demonstrating effectiveness in both reasoning and retrieval.Our code is available at https://github.com/xingxuanli/CR-Planner.

Xingxuan Li, Xuan-Phi Nguyen, Shafiq Joty, Lidong Bing. 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.

In this work, we designed unbiased prompts to systematically evaluate the psychological safety of large language models (LLMs).First, we tested five different LLMs by using two personality tests: Short Dark Triad (SD-3) and Big Five Inventory (BFI).All models scored higher than the human average on SD-3, suggesting a relatively darker personality pattern.Despite being instruction fine-tuned with safety metrics to reduce toxicity, InstructGPT, GPT-3.5, and GPT-4 still showed dark personality patterns; these models scored higher than self-supervised GPT-3 on the Machiavellianism and narcissism traits on SD-3.Then, we evaluated the LLMs in the GPT series by using well-being tests to study the impact of fine-tuning with more training data.We observed a continuous increase in the well-being scores of GPT models.Following these observations, we showed that finetuning Llama-2-chat-7B with responses from BFI using direct preference optimization could effectively reduce the psychological toxicity of the model.Based on the findings, we recommended the application of systematic and comprehensive psychological metrics to further evaluate and improve the safety of LLMs.Our code is available at https://github.com/DAMO- NLP-SG/PsychSafety.Warning: This paper contains examples with potentially harmful content.

We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It results in more factual rationales and reduced hallucination in generation. Specifically, CoK consists of three stages: reasoning preparation, dynamic knowledge adapting, and answer consolidation. Given a knowledge-intensive question, CoK first prepares several preliminary rationales and answers while identifying the relevant knowledge domains. If there is no majority consensus among the answers from samples, CoK corrects the rationales step by step by adapting knowledge from the identified domains. These corrected rationales can plausibly serve as a better foundation for the final answer consolidation. Unlike prior studies that primarily use unstructured data, CoK also leverages structured knowledge sources such as Wikidata and tables that provide more reliable factual information. To access both unstructured and structured knowledge sources in the dynamic knowledge adapting stage, we propose an adaptive query generator that allows the generation of queries for various types of query languages, including SPARQL, SQL, and natural sentences. Moreover, to minimize error propagation between rationales, CoK corrects the rationales progressively using preceding corrected rationales to generate and correct subsequent rationales. Extensive experiments show that CoK consistently improves the performance of LLMs on knowledge-intensive tasks across different domains.

Due to the huge amount of parameters, finetuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.In this work, we present a novel method that operates on the hidden representations of a PLM to reduce overfitting.During fine-tuning, our method inserts random autoencoders between the hidden layers of a PLM, which transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers.The autoencoders are plugged out after fine-tuning, so our method does not add extra parameters or increase computation cost during inference.Our method demonstrates promising performance improvement across a wide range of sequenceand token-level low-resource NLP tasks.Our code is available at https://github.com/DAMO- NLP-SG/MVCR.

As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness.Generating unfactual texts not only leads to lower performances but also degrades the trust and validity of their applications.Chain-of-Thought (CoT) prompting improves trust and model performance on complex reasoning tasks by generating interpretable reasoning chains, but still suffers from factuality concerns in knowledge-intensive tasks.In this paper, we propose the Verify-and-Edit framework for CoT prompting, which seeks to increase prediction factuality by post-editing reasoning chains according to external knowledge.Building on top of GPT-3, our framework lead to accuracy improvements in multiple open-domain question-answering tasks.For reproducing our results and extending the framework further, we make our codebase available at https://github.com/RuochenZhao/Verify- and-Edit * Equal contribution.