China

Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. This paper addresses how to best represent multi-modal longitudinal experiential data as text and formulate prediction tasks to maximize large language model (LLM) cross-distribution generalization. We propose ConText-LE, a framework grounded in linguistic and cognitive theories of contextual meaning-making, which systematically investigates text representation strategies and output formulations for robust behavioral forecasting. Our novel Meta-Narrative representation synthesizes complex temporal patterns into semantically rich narratives, while Prospective Narrative Generation reframes prediction as a generative task aligned with LLMs&#39; inherent contextual understanding capabilities. Through comprehensive experiments on three diverse longitudinal datasets, we address the critical but underexplored challenge of cross-distribution generalization in mental health and educational behavior forecasting. We demonstrate that combining Meta-Narrative input with Prospective Narrative Generation significantly outperforms existing LLM-based approaches, achieving up to 12.28% improvement in out-of-distribution accuracy and up to 11.99% improvement in F1 scores over binary classification methods. Bidirectional evaluation and architectural ablation studies confirm the robustness of our approach, establishing ConText-LE as an effective framework for developing reliable behavioral forecasting systems across temporal and contextual shifts.

EMNLP 2025

ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations

behavioral forecasting

temporal pattern analysis

educational analytics

mental health applications

narrative representations

cross-distribution generalization

longitudinal data modeling

behavioral prediction

contextual understanding

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. This paper addresses how to best represent multi-modal longitudinal experiential data as text and formulate prediction tasks to maximize large language model (LLM) cross-distribution generalization. We propose ConText-LE, a framework grounded in linguistic and cognitive theories of contextual meaning-making, which systematically investigates text representation strategies and output formulations for robust behavioral forecasting. Our novel Meta-Narrative representation synthesizes complex temporal patterns into semantically rich narratives, while Prospective Narrative Generation reframes prediction as a generative task aligned with LLMs' inherent contextual understanding capabilities. Through comprehensive experiments on three diverse longitudinal datasets, we address the critical but underexplored challenge of cross-distribution generalization in mental health and educational behavior forecasting. We demonstrate that combining Meta-Narrative input with Prospective Narrative Generation significantly outperforms existing LLM-based approaches, achieving up to 12.28% improvement in out-of-distribution accuracy and up to 11.99% improvement in F1 scores over binary classification methods. Bidirectional evaluation and architectural ablation studies confirm the robustness of our approach, establishing ConText-LE as an effective framework for developing reliable behavioral forecasting systems across temporal and contextual shifts.

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user’s preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness in LLMs, along with a new dataset, PERGData. We evaluate twelve state-of-the-art models across four prompting strategies. Our findings show that current LLMs struggle with robust personalization: even the strongest model (LLaMA3-70B) fails to maintain correctness in 5% of previously successful cases without personalization, while smaller models (e.g., 7B-scale) can fail more than 20% of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25% across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments.

Benchmarking and Improving LLM Robustness for Personalized Generation

Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)'s influential work on the New Yorker Cartoon Caption Contest (NYCCC). Their study exposed a substantial gap between LLMs and humans in humor comprehension, establishing that understanding and evaluating creative content is key challenge in AI development. We revisit this challenge by decomposing humor understanding into three components and systematically improve each: enhancing visual understanding through improved annotation, utilizing LLM-generated humor reasoning and explanations, and implementing targeted alignment with human preference data. Our refined approach achieves 82.4% accuracy in caption ranking, significantly improving upon the previous 67% benchmark and matching the performance of world-renowned human experts in this domain. Notably, while attempts to mimic subgroup preferences through various persona prompts showed minimal impact, model finetuning with crowd preferences proved remarkably effective. These findings reveal that LLM limitations in creative judgment can be effectively addressed through focused alignment to specific subgroups and individuals. Lastly, we propose the position that achieving artificial general intelligence necessitates systematic collection of human preference data across creative domains. We advocate that just as human creativity is deeply influenced by individual and cultural preferences, training LLMs with diverse human preference data may be essential for developing true creative understanding.

Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs

Alignment of large language models with explicit principles (such as helpfulness, honesty, and harmlessness) is crucial for ensuring safe and reliable AI systems. However, standard reward-based alignment methods typically collapse diverse feedback into a single scalar reward, entangling multiple objectives into one opaque training signal, which hinders interpretability. In this work, we introduce QA-LIGN, an automatic symbolic reward decomposition approach that preserves the structure of each constitutional principle within the reward mechanism. Instead of training a black-box reward model that outputs a monolithic score, QA-LIGN formulates principle-specific evaluation questions and derives separate reward components for each principle, making it a drop-in reward model replacement. Experiments aligning an uncensored large language model with a set of constitutional principles demonstrate that QA-LIGN offers greater transparency and adaptability in the alignment process. At the same time, our approach achieves performance on par with or better than a DPO baseline. Overall, these results represent a step toward more interpretable and controllable alignment of language models, achieved without sacrificing end-task performance.

QA‑LIGN: Aligning LLMs through Constitutionally Decomposed QA

Controlled paraphrase generation produces rephrasings that preserve meaning while enabling targeted stylistic and linguistic modifications. We introduce LingConv, an encoder-decoder framework supporting fine-grained control over 40 linguistic attributes in English. A novel inference-time quality control mechanism iteratively refines attribute embeddings, yielding paraphrases that closely match target properties without sacrificing semantic fidelity. LingConv reduces attribute error by up to 34% over baselines, with quality control providing an additional 14% improvement.

Linguistically-Controlled Paraphrase Generation

Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English dialects. We analyze the effects of typifying "standard" American English language questions as non-"standard" dialectal variants on multiple choice question answering tasks and find up to a 20% reduction in accuracy. Additionally, we investigate the grammatical basis of under-performance in non-"standard" English questions. We find that individual grammatical rules have varied effects on performance, but some are more consequential than others: three specific grammar rules (existential “it,” zero copula, and y'all) can explain the majority of performance degradation observed in multiple dialects. We call for future work to investigate bias mitigation methods focused on individual, high-impact grammatical structures.

Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks

We introduce a Neural-Symbolic Task Planning framework integrating Large Language Model (LLM) decomposition with category-theoretic verification for resource-aware, temporally consistent planning. Our approach represents states as objects and valid operations as morphisms in a categorical framework, ensuring constraint satisfaction through mathematical pullbacks. We employ bidirectional search that simultaneously expands from initial and goal states, guided by a learned planning distance function that efficiently prunes infeasible paths. Empirical evaluations across three planning domains demonstrate that our method outperforms existing baselines by up to 26.1% in completion rates while reducing relative resource violation rate by up to 77%. These results highlight the synergy between LLM-based operator generation and category-theoretic verification for reliable planning in domains requiring both resource-awareness and temporal consistency.

A Category-Theoretic Approach to Neural-Symbolic Task Planning with Bidirectional Search

Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user instructions in the observed physical environment, can lead to navigation errors, such as searching for a refrigerator that does not exist. In this paper, we present the first systematic study of hallucinations in LLM-based embodied agents performing long-horizon tasks under scene–task inconsistencies. Our goal is to understand to what extent hallucinations occur, what types of inconsistencies trigger them, and how current models respond. To achieve these goals, we construct a hallucination probing set by building on an existing benchmark, capable of inducing hallucination rates up to 40times higher than base prompts. Evaluating 11 models across two simulation environments, we find that while models exhibit reasoning, they fail to resolve scene-task inconsistencies — highlighting fundamental limitations in handling infeasible tasks. We also provide actionable insights on ideal model behavior for each scenario, offering guidance for developing more robust and reliable planning strategies.

HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models

Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems. This paper proposes a speech embedding technique that facilitates such a system that we can be used in a zero-shot manner on the target language. After conducting development experiments on several written Indic languages, we evaluate our method on a corpus of Gormati -- an unwritten language -- that was previously collected in partnership with an agrarian Banjara community in Maharashtra State, India, specifically for the purposes of information retrieval. Our system achieves a Top 5 retrieval rate of 87.9% on this data, giving the hope that it may be useable by unwritten language speakers worldwide.

Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati

Like most languages, sign languages evolve over time. It is important that sign language dictionaries' vocabularies are updated over time to reflect these changes, such as by adding new signs. However, most dictionary retrieval methods based upon machine learning models only work with fixed vocabularies, and it is unclear how they might support dictionary expansion without retraining. In this work, we explore the feasibility of dictionary expansion for sign language dictionaries using a simple representation-based method. We explore a variety of dictionary expansion scenarios, e.g., varying number of signs added as well as amount of data for these newly added signs. Through our results, we show how performance varies significantly across different scenarios, many of which are reflective of real-world data challenges. Our findings offer implications for the development & maintenance of video-based sign language dictionaries, and highlight directions for future research on dictionary expansion.

Investigating Dictionary Expansion for Video-based Sign Language Dictionaries

Vision-Language Models have shown impressive capabilities in image understanding tasks but their ability to interpret diverse data visualizations with varying information density remains underexplored. This paper presents a systematic evaluation of VLMs on process visualizations - a class of visualization requiring both visual literacy and sequential reasoning. We evaluate models across three process visualization techniques on multiple tasks. We evaluate extraction performance on charts, and construct a benchmark dataset of expert-validated QA pairs. Our findings reveal that while frontier models achieve strong performance on extraction and single hop reasoning (>80% accuracy), they struggle on multi-hop reasoning tasks. Open-source models struggle with all reasoning tasks. We also perform an analysis of chart information density showing performance decreases for models as information complexity increases.

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