Abstract While large language models (LLMs) have shown remarkable effectiveness in various NLP tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A promising approach to rectify these flaws is correcting LLMs with feedback, where the LLM itself is prompted or guided with feedback to fix problems in its own output. Techniques leveraging automated feedback—either produced by the LLM itself (self-correction) or some external system—are of particular interest as they make LLM-based solutions more practical and deployable with minimal human intervention. This paper provides an exhaustive review of the recent advances in correcting LLMs with automated feedback, categorizing them into training-time, generation-time, and post-hoc approaches. We also identify potential challenges and future directions in this emerging field.

Language Models (LMs) have shown impressive performance in various natural language tasks.However, when it comes to natural language reasoning, LMs still face challenges such as hallucination, generating incorrect intermediate reasoning steps, and making mathematical errors.Recent research has focused on enhancing LMs through self-improvement using feedback.Nevertheless, existing approaches relying on a single generic feedback source fail to address the diverse error types found in LMgenerated reasoning chains.In this work, we propose Multi-Aspect Feedback, an iterative refinement framework that integrates multiple feedback modules, including frozen LMs and external tools, each focusing on a specific error category.Our experimental results demonstrate the efficacy of our approach to addressing several errors in the LM-generated reasoning chain and thus improving the overall performance of an LM in several reasoning tasks.We see a relative improvement of up to 20% in Mathematical Reasoning and up to 18% in Logical Entailment.We release our source code, prompts, and data 1 to accelerate future research.

Style transfer is the task of rewriting a sentence into a target style while approximately preserving content.While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al., 2021) has attempted "few-shot" style transfer using just 3-10 sentences at inference for style extraction.In this work, we study a relevant low-resource setting: style transfer for languages where no style-labelled corpora are available.We notice that existing few-shot methods perform this task poorly, often copying inputs verbatim.We push the state-of-the-art for few-shot style transfer with a new method modeling the stylistic difference between paraphrases.When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages.Moreover, our method is better at controlling the style transfer magnitude using an input scalar knob.We report promising qualitative results for several attribute transfer tasks (sentiment transfer, simplification, gender neutralization, text anonymization) all without retraining the model.Finally, we find model evaluation to be difficult due to the lack of datasets and metrics for many languages.To facilitate future research we crowdsource formality annotations for 4000 sentence pairs in four Indic languages, and use this data to design our automatic evaluations. 1

We design and implement a personalized and automated physical activity coaching engine, PACE, which uses the Fogg’s behavioral model (FBM) to engage users in mini-conversation based coaching sessions. It is a chat-based nudge assistant that can boost (encourage) and sense (ask) the motivation, ability and propensity of users to walk and help them in achieving their step count targets, similar to a human coach. We demonstrate the feasibility, effectiveness and acceptability of PACE by directly comparing to human coaches in a Wizard-of-Oz deployment study with 33 participants over 21 days. We tracked coach-participant conversations, step counts and qualitative survey feedback. Our findings indicate that the PACE framework strongly emulated human coaching with no significant differences in the overall number of active days, step count and engagement patterns. The qualitative user feedback suggests that PACE cultivated a coach-like experience, offering barrier resolution via motivational and educational support. We use traditional human-computer interaction approaches, to interrogate the conversational data and report positive PACE-participant interaction patterns with respect to addressal, disclosure, collaborative target settings, and reflexivity. As a post-hoc analysis, we annotated the conversation logs from the human coaching arm and trained machine learning (ML) models on these data sets to predict the next boost (AUC 0.73 ± 0.02) and sense (AUC 0.83 ± 0.01) action. In future, such ML-based models could be made increasingly personalized and adaptive based on user behaviors.

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction.However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple.To this effect, our paper proposes a novel attention-based feature embedding that captures both entity and relation features in any given entity's neighborhood.Additionally, we also encapsulate relation clusters and multi-hop relations in our model.Our empirical study offers insights into the efficacy of our attention-based model and we show marked performance gains in comparison to state-of-the-art methods on all datasets.

We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure. The proposed method creates random clique adjacency matrices for each k-skeleton of the random clique complexes and matches them, taking into account each point as the affine combination of its geometric neighborhood. We justify our solution theoretically, by analyzing the runtime and storage complexity of our algorithm along with the asymptotic behavior of the quadratic assignment problem (QAP) that is associated with the underlying random clique adjacency matrices. Experiments on both synthetic and real-world datasets, containing severe occlusions and distortions, provide insight into the accuracy, efficiency, and robustness of our approach. We outperform diverse matching algorithms by a significant margin.