United States

Theorem proving presents a significant chal- lenge for large language models (LLMs) due to the requirement for formal proofs to be rig- orously checked by proof assistants, such as Lean, eliminating any margin for error or hallu- cination. While existing LLM-based theorem provers attempt to operate autonomously, they often struggle with novel and complex theo- rems where human insights are essential. Lean Copilot is a novel framework that integrates LLM inference into the Lean proof assistant environment. In this work, we benchmark per- formance of several LLMs including general and math-specific models for theorem proving using the Lean Copilot framework. Our initial investigation suggests that a general-purpose large model like LLaMa-70B still has edge over math-specific smaller models for the task under consideration. We provide useful insights into the performance of different LLMs we chose for the task.

EMNLP 2024

Benchmarking Automated Theorem Proving with Large Language Models

ai for science

interactive theorem proving

automated theorem proving

large language models

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

When building models of human behavior, we often struggle to find data that capture important factors at the right level of granularity. In these cases, we must rely on expert knowledge to build models. To help partially automate the organization of expert knowledge for modeling, we combine natural language processing (NLP) and machine learning (ML) methods in a tool called the Grid. The Grid helps users organize textual knowledge into clickable cells along two dimensions using iterative, collaborative clustering. We conduct a user study to explore participants' reactions to the Grid, as well as to investigate whether its clustering feature helps participants organize a corpus of expert knowledge. We find that participants using the Grid's clustering feature appeared to work more efficiently than those without it, but written feedback about the clustering was critical. We conclude that the general design of the Grid was positively received and that some of the user challenges can likely be mitigated through the use of LLMs.

The Grid: A semi-automated tool to support expert-driven modeling

There has been a huge number of benchmarks proposed to evaluate how large language models (LLMs) behave for logic inference tasks. However, it remains an open question how to properly evaluate this ability. In this paper, we provide a systematic overview of prior works on the logical reasoning ability of LLMs for analyzing categorical syllogisms. We first investigate all the possible variations for categorical syllogisms from a purely logical perspective and then examine the underlying configurations (i.e., mood and figure) tested by existing datasets. Our results indicate that compared to template-based synthetic datasets, crowdsourcing approaches normally sacrifice the coverage of configurations (i.e., mood and figure) of categorical syllogisms for more language variations, thus bringing challenges to fully testing LLMs under different situations. We then summarize the findings and observations for the performance of LLMs to infer the validity of syllogisms from the current literature. The error rate breakdown analyses suggest that the interpretation of quantifiers seems to be the current bottleneck that limits the performance of the LLMs and is thus worth more attention. Finally, we discuss several points that might be worth considering when researchers plan to release categorical syllogism datasets. We hope our work will provide a timely review of the current literature regarding categorical syllogisms, and motivate more interdisciplinary research between communities, specifically computational linguists and logicians.

Categorical Syllogisms Revisited: A Review of the Logical Reasoning Abilities of LLMs for Analyzing Categorical Syllogisms

Integrating cognitive ergonomics with LLMs is crucial for improving safety, reliability, and user satisfaction in human-AI interactions. Current LLM designs often lack this integration, resulting in systems that may not fully align with human cognitive capabilities and limitations. This oversight exacerbates biases in LLM outputs and leads to suboptimal user experiences due to inconsistent application of user-centered design principles. Researchers are increasingly leveraging NLP, particularly LLMs, to model and understand human behavior across social sciences, psychology, psychiatry, health, and neuroscience. Our position paper explores the need to integrate cognitive ergonomics into LLM design, providing a comprehensive framework and practical guidelines for ethical development. By addressing these challenges, we aim to advance safer, more reliable, and ethically sound human-AI interactions.

CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics

This paper investigates how the topical flow of dyadic conversations emerges over time and how differences in interlocutors' personality traits contribute to this topical flow. Leveraging text embeddings, we map the trajectories of N=1655 conversations between strangers into a high-dimensional space. Using nonlinear projections and clustering, we then identify when each interlocutor enters and exits various topics. Differences in conversational flow are quantified via topic entropy, a summary measure of the "spread" of topics covered during a conversation, and linguistic alignment, a time-varying measure of the cosine similarity between interlocutors' embeddings. Our findings suggest that interlocutors with a larger difference in the personality dimension of openness influence each other to spend more time discussing a wider range of topics and that interlocutors with a larger difference in extraversion experience a larger decrease in linguistic alignment throughout their conversation. We also examine how participants' affect (emotion) changes from before to after a conversation, finding that a larger difference in extraversion predicts a larger difference in affect change and that a greater topic entropy predicts a larger affect increase. This work demonstrates how communication research can be advanced through the use of high-dimensional NLP methods and identifies personality difference as an important driver of social influence.

Personality Differences Drive Conversational Dynamics: A High-Dimensional NLP Approach

Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability to generate proactive responses that actively engage users in sustained conversations. However, existing definitions of proactive dialogue in this context do not focus on how each response actively engages the user and sustains the conversation. Hence, we present a new definition of proactivity that focuses on enhancing the `proactiveness' of each generated response via the introduction of new information related to the initial query. To this end, we construct a proactive dialogue dataset comprising 2,000 single-turn conversations, and introduce several automatic metrics to evaluate response 'proactiveness' which achieved high correlation with human annotation. Additionally, we introduce two innovative Chain-of-Thought (CoT) prompts, the 3-step CoT and the 3-in-1 CoT prompts, which consistently outperform standard prompts by up to 90% in the zero-shot setting.

Redefining Proactivity for Information Seeking Dialogue

Code-mixing (CM), where speakers blend languages within a single expression, is prevalent in multilingual societies but poses challenges for natural language processing due to its complexity and limited data. We propose using a large language model to generate synthetic CM data, which is then used to enhance the performance of task-specific models for CM sentiment analysis. Our results show that in Spanglish, synthetic data improved the F1 score by 9.32\%, outperforming previous augmentation techniques. However, in Malayalam-English, synthetic data only helped when the baseline was low; with strong natural data, additional synthetic data offered little benefit. Human evaluation confirmed that this approach is a simple, cost-effective way to generate natural-sounding CM sentences, particularly beneficial for low baselines. Our findings suggest that few-shot prompting of large language models is a promising method for CM data augmentation and has significant impact on improving sentiment analysis, an important element in the development of social influence systems.

Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis

The unchecked spread of digital information, combined with increasing political polariza- tion and the tendency of individuals to iso- late themselves from opposing political view- points, has driven researchers to develop sys- tems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore methods for categorizing bias in US news arti- cles, comparing rule-based and deep learning approaches. The study highlights the sensi- tivity of modern self-learning systems to un- constrained data ingestion, while reconsider- ing the strengths of traditional rule-based sys- tems. Applying both models to left-leaning (CNN) and right-leaning (FOX) news articles, we assess their effectiveness on data beyond the original training and test sets. This analy- sis highlights each model’s accuracy, offers a framework for exploring deep-learning explain- ability, and sheds light on political bias in US news media. We contrast the opaque architec- ture of a deep learning model with the trans- parency of a linguistically informed rule-based model, showing that the rule-based model per- forms consistently across different data condi- tions and offers greater transparency, whereas the deep learning model is dependent on the training set and struggles with unseen data.

Balancing Transparency and Accuracy: A Comparative Analysis of Rule-Based and Deep Learning Models in Political Bias Classification

AI chatbots are increasingly designed to exhibit human-like traits such as empathy. However, how users perceive the empathy of these chatbots, and how this affects users' feelings about the conversation with them, has not been thoroughly examined. This study examines how the identity and perceived empathy of a chatbot affect human perceptions of the overall conversation quality. We analyzed 154 conversations from two datasets and found that, although GPT-based chatbots were rated significantly higher in overall conversational quality, they were perceived by users as having lower empathy compared to their human counterparts. Ratings of empathy from GPT-4o annotation align with human assessments, demonstrating a consistent perception of lower empathy in chatbots. However, off-the-shelf empathy models trained human-human conversations found no differences in perceived empathy in language between chatbots and humans. Our research emphasizes the importance of perceived empathy in influencing overall conversation quality. Managing perceived empathy in human-AI interactions is complex; creating chatbots that are perceived as high-conversational quality involves more than simply using empathetic language.

The Illusion of Empathy: How AI Chatbots Shape Conversation Perception

Large Language Models (LLMs) excel at a range of tasks but often struggle with issues like hallucination and inadequate empathy support. To address hallucinations, we ground our dialogues in medical knowledge sourced from external repositories such as Disease Ontology and DrugBank. To improve empathy support, we develop the Empathetic Healthcare Dialogues dataset, which utilizes multiple dialogue strategies in each response. This dataset is then used to fine-tune an LLM, and we introduce a lightweight, adaptable method called Strategy Combination Guidance to enhance the emotional support capabilities of the fine-tuned model, named EHDChat. Our evaluations show that EHDChat significantly outperforms existing models in providing emotional support and medical accuracy, demonstrating the effectiveness of our approach in enhancing empathetic and informed AI interactions in healthcare.

EHDChat: A Knowledge-Grounded, Empathy-Enhanced Language Model for Healthcare Interactions

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. However, existing benchmarks are limited to two domains and do not evaluate model performance on unseen domains, raising concerns about the generalization of proposed methods. Furthermore, it remains unclear if large language models (LLMs) can effectively handle complex sentiment tasks like ASTE. In this work, we address the issue of generalization in ASTE from both a benchmarking and modeling perspective. We introduce a domain-expanded benchmark by annotating samples from diverse domains, enabling evaluation of models in both in-domain and out-of-domain settings. Additionally, we propose CASE, a simple and effective decoding strategy that enhances trustworthiness and performance of LLMs in ASTE. Through comprehensive experiments involving multiple tasks, settings, and models, we demonstrate that CASE can serve as a general decoding strategy for complex sentiment tasks. By expanding the scope of evaluation and providing a more reliable decoding strategy, we aim to inspire the research community to reevaluate the generalizability of benchmarks and models for ASTE. Our code, data, and models are available at https://github.com/DAMO-NLP-SG/domain-expanded-aste.

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