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Citation context analysis (CCA) is a field of research studying the role and purpose of citation in scientific discourse. While most of the efforts in CCA have been focused on elaborate characterization schemata to assign function or intent labels to individual citations, the citation context as the basis for such a classification has received rather limited attention. This relative neglect, however, has led to the precedence of vague definitions and restricting assumptions, limiting the citation context in its expressiveness. It is a common practice, for example, to restrict the context to the citing sentence. While this might be enough to cover mentions and background citations, more influential ones are often thoroughly discussed, extending beyond a one-sentence context window. To address this concern, we analyze the semantic structure of citation contexts in terms of their elemental dimensions and distribution throughout the citing text. To evaluate this approach, we construct and publish the FineCite Corpus containing 1,056 manually annotated fine-grained citation contexts. Our experiments on established CCA benchmarks demonstrate the effectiveness of our finer-grained context definition, showing improvement compared to state-of-the-art approaches. We will release our code and dataset to the public upon acceptance.

ACL 2025

FINECITE: A Novel Approach on Fine-Grained Citation Context Analysis

scientific argumentation

citation context analysis

information extraction

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IJCNLP-AACL 2022

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Real-world applications of KBQA require models to handle unanswerable questions with a limited volume of in-domain labeled training data. We propose the novel task of few-shot transfer for KBQA with unanswerable questions and contribute two new datasets for performance evaluation. We present FUn-FuSIC -- a novel solution for our task that extends FuSIC-KBQA, the state-of-the-art few-shot transfer model for answerable-only KBQA. We first note that FuSIC-KBQA's iterative repair makes a strong assumption that all questions are answerable. As a remedy, we propose Feedback for Unanswerability (FUn), which uses iterative repair using feedback from a suite of strong and weak verifiers, and an adaptation of self-consistency for unanswerabilty to better assess the answerability of a question. Our experiments show that FUn-FuSIC significantly outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on our task, while establishing a new SoTA for answerable few-shot transfer as well.

An Iterative Repair Approach for Few-shot Transfer in KBQA with Unanswerability

Small large language models (sLLMs) offer the advantage of being lightweight and efficient, which makes them suitable for resource-constrained environments. However, sLLMs often struggle to maintain topic consistency in task-oriented dialogue systems, which is critical for scenarios such as service chatbots. Specifically, it is important to ensure that the model denies off-topic or malicious inputs and adheres to its intended functionality so as to prevent potential misuse and uphold reliability. Towards this, existing activation engineering approaches have been proposed to manipulate internal activations during inference. While these methods are effective in certain scenarios, our preliminary experiments reveal their limitations in ensuring topic adherence. Therefore, to address this, we propose a novel approach termed \textbf{En}tropy-scaled \textbf{S}teering vectors for \textbf{To}pic \textbf{M}aintenance (EnSToM). EnSToM dynamically adjusts the steering intensity based on input uncertainty, which allows the model to handle off-topic distractors effectively while preserving on-topic accuracy. Our experiments demonstrate that EnSToM achieves significant performance gain with a relatively small data size compared to fine-tuning approaches. By improving topic adherence without compromising efficiency, our approach provides a robust solution for enhancing sLLM-based dialogue systems\footnote{We will release our source code upon acceptance}.

EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance

Programming with a coding assistant is a fundamentally interactive process, yet existing static benchmarks fail to capture key features of model-user collaboration. We introduce an interactive evaluation pipeline to examine how LLMs incorporate different types of feedback in a collaborative setting, in which we obfuscate the input of static coding benchmarks so that the code model must interact with a simulated user. Across 10 models and 3 datasets, the relative rankings of models often permute greatly between static and interactive settings, despite models being fairly robust to feedback that contains errors. We also observe that similarly effective feedback types differ in terms of how models respond to higher- vs. lower-quality feedback. Moreover, feedback type impacts the degree to which the models make aesthetic or behavioral edits to their output. Our work aims to "re-evaluate" model coding capabilities through an interactive lens toward bridging the gap between existing evaluations and real-world usage.

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback

We introduce "Generative Fusion Decoding'' (GFD), a novel shallow fusion framework designed to integrate large language models (LLMs) into cross-modal text recognition systems, such as automatic speech recognition (ASR) and optical character recognition (OCR). GFD is methodologically orthogonal to prior work and operates across mismatched token spaces of different models by calculating likelihood at the byte level, enabling seamless fusion during the decoding process. This plug-and-play framework is compatible with various auto-regressive models and does not require re-training for feature alignment or two-pass strategies. GFD offers two key benefits with LLM interaction: effective domain adaptation using in-context learning, and maintaining contextual semantics over long-form audio. These advantages have been shown to effectively reduce error rates for ASR in both English and Chinese. Additionally, GFD's extensibility goes beyond prior work as exhibited in significant improvement on instructed speech recognition tasks and OCR, which have not been reported before. Finally, we provide a time complexity analysis demonstrating the efficiency of GFD, highlighting its potential in real-time error correction. Overall, GFD parallely integrates LLMs into text recognition systems, enhancing accuracy and domain adaptativeness while maintaining decoding efficiency.

Let’s Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Robust and Instruction-Aware ASR and OCR

The conversational capabilities of Large Language Models (LLMs) suggest that they may be able to perform as automated talk therapists. It is crucial to know if these systems would be effective and adhere to known standards. We present a counsellor chatbot that focuses on motivating tobacco smokers to quit smoking. It uses a state-of-the-art LLM and a widely applied therapeutic approach called Motivational Interviewing (MI), and was evolved in collaboration with clinician-scientists with expertise in MI. We also describe and validate an automated assessment of both the chatbot's adherence to MI and client responses. The chatbot was tested on 106 participants, and their confidence that they could succeed in quitting smoking was measured before the conversation and one week later. Participants' confidence increased by an average of 1.7 on a 0-10 scale. The automated assessment of the chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors. The chatbot scored well on a participant-reported metric of perceived empathy but lower than typical human counsellors. Furthermore, participants' language indicated a good level of motivation to change, a key goal in MI. These results suggest that the automation of talk therapy with a modern LLM has promise.

A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit

An increasingly prevalent socio-technical problem is people being taken in by offers that sound “too good to be true”, where persuasion and trust shape decision-making. This paper investigates how AI can help detect these deceptive scenarios. We analyze how humans strategically deceive each other in Diplomacy, a board game that requires both natural language communication and strategic reasoning. This requires extracting logical forms of proposed agreements in player communications and computing the relative rewards of the proposal using agents’ value functions. Combined with text-based features, this can improve our deception detection. Our method detects human deception with a high precision when compared to a Large Language Model approach that flags many true messages as deceptive. Future human-AI interaction tools can build on our methods for deception detection by triggering friction to give users a chance of interrogating suspicious proposals.

Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL

Recent work has suggested that overlap –whether a given lemma or feature set is attested independently in train – drives model performance on morphological inflection tasks. The impact of lemma overlap, however, is debated, with recent work reporting accuracy drops from 0% to 30% between seen and unseen test lemmas. In this paper, we introduce a novel splitting algorithm designed to investigate predictors of accuracy on seen and unseen lemmas. We find only an 11% average drop from seen to unseen test lemmas, but show that the number of lemmas in train has a much stronger effect on accuracy on unseen than seen lemmas. We also show that the previously reported 30% drop is inflated due to the introduction of a near-30% drop in the number of training lemmas from the original splits to their novel splits.

Lemmas Matter, But Not Like That: Predictors of Lemma-Based Generalization in Morphological Inflection

Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents, especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning and high-quality human feedback. However, as base LLM models continue to improve, acquiring meaningful human feedback has become increasingly challenging and costly. In certain domains, base LLM agents may eventually exceed human capabilities, making traditional feedback-driven methods impractical. In this paper, we introduce a novel self-improvement paradigm that empowers LLM agents to autonomously enhance their performance without external human feedback. Our method, Juxtaposed Outcomes for Simulation Harvesting (JOSH), is a self-alignment algorithm that leverages a sparse reward simulation environment to extract ideal behaviors and further train the LLM on its own outputs. We present ToolWOZ, a sparse reward tool-calling simulation environment derived from MultiWOZ. We demonstrate that models trained with JOSH, both small and frontier, significantly improve tool-based interactions while preserving general model capabilities across diverse benchmarks. Our code and data are publicly available on GitHub.

Sparse Rewards Can Self-Train Dialogue Agents

Despite the widespread use of 'artificial intelligence' (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by ''intelligence''. To that end, we present the results of a survey on the notion of ''intelligence'' among researchers and its role in the research agenda. The survey elicited complete responses from 303 researchers from a variety of fields including NLP, Machine Learning (ML), Cognitive Science, Linguistics, and Neuroscience.
We identify 3 criteria of intelligence that the community agrees on the most: generalization, adaptability, \& reasoning.
Our results suggests that the perception of the current NLP systems as ''intelligent'' is a minority position (29%).
Furthermore, only 16.2% of the respondents see developing intelligent systems as a research goal, and these respondents are more likely to consider the current systems intelligent.

Research Community Perspectives on "Intelligence" and Large Language Models

In this paper, we propose the first multilingual study on definition modeling. We use monolingual dictionary data for four new languages (Spanish, French, Portuguese, and German) and perform an in-depth empirical study to test the performance of pre-trained multilingual language models on definition modeling of monosemic words when finetuned on this data. Furthermore, we use a zero-shot approach to test the multilingual capabilities of two popular chat-based Large Language Models (LLMs) in the task. Results show that multilingual language models can perform on-pair with English but cannot leverage potential cross-lingual synergies, with LLMs generally offering better performance overall. A comprehensive human evaluation of the LLM-generated definition highlights the zero and few-shot capabilities of these models in this new task, also showing their shortcomings. Finally, we show that performance on our task via BERTScore strongly correlates to the performance on multilingual LLM benchmarks, suggesting that our task offers a viable compute-constrained, stable and natural alternative to these.

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An Iterative Repair Approach for Few-shot Transfer in KBQA with Unanswerability

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Dynamic Context Extraction for Citation Classification

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