ymoslem/acl-abstracts huggingface dataset:
Ignore the KL Penalty! Boosting Exploration on Critical Tokens to Enhance RL Fine-Tuning (2025)

The ability to achieve long-term goals is a key challenge in the current development of large language models (LLMs). To address this, pre-trained LLMs can be fine-tuned with reinforcement learning (RL) to explore solutions that optimize a given goal. However, exploration with LLMs is difficult, as a balance has to be struck between discovering new solutions and staying close enough to the pre-trained model, so as not to degrade basic capabilities. This is typically controlled with a Kullback-Leibler (KL) penalty. In this paper, we investigate the exploration dynamics of a small language model on a simple arithmetic task. We show how varying degrees of pre-training influence exploration and demonstrate the importance of {\textquotedblleft}critical tokens{\textquotedblright} which have a dramatic impact on the final outcome. Consequently, we introduce a simple modification to the KL penalty that favors exploration on critical tokens, increasing the efficiency of the RL fine-tuning stage.
SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia (2025)

This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real-world scenarios from SEA regions. SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses subjects such as local history and literature. In contrast, SeaBench is crafted around multi-turn, open-ended tasks that reflect daily interactions within SEA communities. Our evaluations demonstrate that SeaExam and SeaBench more effectively discern LLM performance on SEA language tasks compared to their translated benchmarks. This highlights the importance of using real-world queries to assess the multilingual capabilities of LLMs.
Learning to Search Effective Example Sequences for In-Context Learning (2025)

Large language models (LLMs) demonstrate impressive few-shot learning capabilities, but their performance varies widely based on the sequence of in-context examples. Key factors influencing this include the sequence`s length, composition, and arrangement, as well as its relation to the specific query. Existing methods often tackle these factors in isolation, overlooking their interdependencies. Moreover, the extensive search space for selecting optimal sequences complicates the development of a holistic approach. In this work, we introduce Beam Search-based Example Sequence Constructor (BESC), a novel method for learning to construct optimal example sequences. addresses all key factors involved in sequence selection by considering them jointly during inference, while incrementally building the sequence. This design enables the use of beam search to significantly reduce the complexity of the search space. Experiments across various datasets and language models show notable improvements in performance.
From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models (2025)

With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Watermarking offers a vital solution, protecting both - LLM-generated and plain text sources. This paper presents a unified overview of different perspectives behind designing watermarking techniques through a comprehensive survey of the research literature. Our work has two key advantages: (1) We analyze research based on the specific intentions behind different watermarking techniques, evaluation datasets used, and watermarking addition and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research protecting text authorship. This extensive coverage and detailed analysis sets our work apart, outlining the evolving landscape of text watermarking in Language Models.
M-IFEval: Multilingual Instruction-Following Evaluation (2025)

Instruction following is a core capability of modern Large language models (LLMs), making evaluating this capability essential to understanding these models. The Instruction Following Evaluation (IFEval) benchmark from the literature does this using objective criteria, offering a measure of LLM performance without subjective AI or human judgement. However, it only includes English instructions, limiting its ability to assess LLMs in other languages.We propose the Multilingual Instruction Following Evaluation (M-IFEval) benchmark, expanding the evaluation to French, Japanese, and Spanish, with both general and language-specific instructions. Applying this benchmark to 8 state-of-the-art LLMs, we find that benchmark performance across languages and instruction types can vary widely, underscoring the importance of a multilingual benchmark for evaluating LLMs in a diverse cultural context.
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language (2025)

Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. This paper introduces LA$^3$, a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets, thereby improving AI training. We demonstrate the effectiveness of LA$^3$ by creating an enhanced dataset, LaChEBI-20, where we systematically rewrite the annotations of molecules from an established dataset. These rewritten annotations preserve essential molecular information while providing more varied sentence structures and vocabulary. Using LaChEBI-20, we train LaMolT5 based on a benchmark architecture to learn the mapping between molecular representations and augmented annotations.Experimental results on text-based *de novo* molecule generation and molecule captioning demonstrate that LaMolT5 outperforms state-of-the-art models. Notably, incorporating LA$^3$ leads to improvements of up to 301{\%} over the benchmark architecture. Furthermore, we validate the effectiveness of LA$^3$ notable applications in *image*, *text* and *graph* tasks, affirming its versatility and utility.
Let Modalities Teach Each Other: Modal-Collaborative Knowledge Extraction and Fusion for Multimodal Knowledge Graph Completion (2025)

Multimodal knowledge graph completion (MKGC) aims to predict missing triples in MKGs using multimodal information. Recent research typically either extracts information from each modality separately to predict, then ensembles the predictions at the decision stage, or projects multiple modalities into a unified feature space to learn multimodal representations for prediction. However, these methods usually overlook the intrinsic correlation between modalities in MKGs which should be leveraged in both unimodal knowledge extraction and multimodal knowledge fusion. Motivated by this, we propose a noval Modal-collaborative knowledge learning (Moodle) framework for MKGC, the key idea of which is to foster mutual guidance and collaboration during unimodal knowledge extraction, to let each modality acquire distinct and complementary knowledge that subsequently enhances the multimodal knowledge fusion. Specifically, Moodle preserves the representations of different modalities to learn unimodal knowledge while modeling the mutual guidance through multi-task learning. Furthermore, Moodle performs multimodal knowledge fusion and prediction guided by unimodal knowledge, capturing their synergistic relationships and acquire fine-grained semantic knowledge through contrastive learning. Extensive experiments on three real-world datasets demonstrate the advantages of Moodle over state-of-the-art methods.
Modeling the Differential Prevalence of Online Supportive Interactions in Private Instant Messages of Adolescents (2025)

This paper focuses on modeling gender-based and pair-or-group disparities in online supportive interactions among adolescents. To address the limitations of conventional social science methods in handling large datasets, this research employs language models to detect supportive interactions based on the Social Support Behavioral Code and to model their distribution. The study conceptualizes detection as a classification task, constructs a new dataset, and trains predictive models. The novel dataset comprises 196,772 utterances from 2165 users collected from Instant Messenger apps. The results show that the predictions of language models can be used to effectively model the distribution of supportive interactions in private online dialogues. As a result, this study provides new computational evidence that supports the theory that supportive interactions are more prevalent in online female-to-female conversations. The findings advance our understanding of supportive interactions in adolescent communication and present methods to automate the analysis of large datasets, opening new research avenues in computational social science.
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks (2025)

This paper presents an efficient dual-stream early fusion method for sign language translation. Inspired by the brain`s ability to process color, shape, and motion simultaneously, the method explores complex dependencies between RGB and keypoint streams, improving speed and efficiency. A key challenge is extracting complementary features from both streams while ensuring global semantic consistency to avoid conflicts and improve generalization. To address this issue, we propose a hypernetwork-based fusion strategy that effectively extracts salient features from RGB and keypoint streams, alongside a partial shortcut connection training method to strengthen the complementary information between the dual streams. Additionally, we introduce self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. Experiments show that our method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds.
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models (2025)

Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting, where the model becomes too specialized in either the task or the characteristics of the training data, resulting in a loss of generalization. This paper introduces Selective Self-to-Supervised Fine-Tuning (S3FT), a fine-tuning approach that achieves better performance than the standard supervised fine-tuning (SFT) while improving generalization.S3FT leverages the existence of multiple valid responses to a query.By utilizing the model`s correct responses, S3FT reduces model specialization during the fine-tuning stage. S3FT first identifies the correct model responses from the training set by deploying an appropriate judge. Then, it fine-tunes the model using the correct model responses and the gold response (or its paraphrase) for the remaining samples.The effectiveness of S3FT is demonstrated through experiments on mathematical reasoning, Python programming and reading comprehension tasks. The results show that standard SFT can lead to an average performance drop of up to 4.4 on multiple benchmarks, such as MMLU and TruthfulQA. In contrast, S3FT reduces this drop by half, i.e. 2.5, indicating better generalization capabilities than SFT while performing significantly better on the fine-tuning tasks.
MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025)

Despite recent advances in Multimodal Relation Extraction (MRE), existing datasets and approaches primarily focus on single-image scenarios, overlooking the prevalent real-world cases where relationships are expressed through multiple images alongside text. To address this limitation, we present MRE-MI, a novel human-annotated dataset that includes both multi-image and single-image instances for relation extraction. Beyond dataset creation, we establish comprehensive baselines and propose a simple model named Global and Local Relevance-Modulated Attention Model (GLRA) to address the new challenges in multi-image scenarios. Our extensive experiments reveal that incorporating multiple images substantially improves relation extraction in multi-image scenarios. Furthermore, GLRA achieves state-of-the-art results on MRE-MI, demonstrating its effectiveness. The datasets and source code can be found at https://github.com/JinFish/MRE-MI.
Discrete Diffusion Language Model for Efficient Text Summarization (2025)

While diffusion models excel at conditionally generating high-quality images, prior works in discrete diffusion models were not evaluated on conditional long-text generation. This work addresses the limitations of prior discrete diffusion models for conditional long-text generation, particularly in the long abstractive summarization task. Despite faster decoding speeds compared to autoregressive methods, previous discrete diffusion models failed on the abstractive summarization task due to the incompatibility between the backbone architectures and the random noising process. To overcome these challenges, we introduce a novel semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. Additionally, we propose CrossMamba, an adaptation of the Mamba model to the encoder-decoder paradigm, which integrates seamlessly with the random absorbing noising process. Our approaches outperform existing discrete diffusion models on three benchmark summarization datasets: Gigaword, CNN/DailyMail, and Arxiv, while also achieving much faster inference speed compared to autoregressive models.
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025)

Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models (2025)

Large language models (LLMs) have achieved remarkable success but still tend to generate factually erroneous responses, a phenomenon known as hallucination. A recent trend is to use preference learning to fine-tune models to align with factuality. However, existing work primarily evaluates fine-tuned models on in-domain (ID) datasets and the factuality on out-of-domain (OOD) datasets remains underexplored. In this paper, we conduct a comprehensive evaluation of the factuality of different models tuned by various preference learning algorithms and demonstrate that their performance on OOD datasets either increases minimally or decreases. Subsequently, we reveal that the main cause of model`s failure to uphold factuality under a distribution shift is \textbf{under-alignment}, rather than \textbf{over-alignment}, by analyzing the token distribution shift of the models before and after tuning. Finally, we propose \textbf{APEFT} (\textbf{A}tomic \textbf{P}reference \textbf{E}nhanced \textbf{F}actuality \textbf{T}uning), a framework that enhances model`s awareness of factuality at the granularity of individual facts. Extensive experiments demonstrate that APEFT improves model performance by an average of on both ID and OOD datasets, which is highly effective.
Weight-based Analysis of Detokenization in Language Models: Understanding the First Stage of Inference Without Inference (2025)

According to the stages-of-inference hypothesis, early layers of language models map their subword-tokenized input, which does not necessarily correspond to a linguistically meaningful segmentation, to more meaningful representations that form the model`s {\textquotedblleft}inner vocabulary{\textquotedblright}.Prior analysis of this *detokenization* stage has predominantly relied on probing and interventions such as path patching, which involve selecting particular inputs, choosing a subset of components that will be patched, and then observing changes in model behavior.Here, we show that several important aspects of the detokenization stage can be understood purely by analyzing model weights, without performing any model inference steps.Specifically, we introduce an analytical decomposition of first-layer attention in GPT-2.Our decomposition yields interpretable terms that quantify the relative contributions of position-related, token-related, and mixed effects.By focusing on terms in this decomposition, we discover weight-based explanations of attention bias toward close tokens and attention for detokenization.
DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking (2025)

Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this paper introduces DiPT, a novel approach that complements current reasoning methods by explicitly incorporating diversified viewpoints. This approach allows the model to gain a deeper understanding of the problem`s context and identify the most effective solution path during the inference stage. Additionally, it provides a general data-centric AI recipe for augmenting existing data to improve their quality for fine-tuning. Our empirical results demonstrate that DiPT can be flexibly integrated into existing methods that focus on a single reasoning approach, enhancing their reasoning performance and stability when presented with paraphrased problems. Furthermore, we illustrate improved context understanding by maintaining the model`s safe outputs against {\textquotedblleft}jailbreaking{\textquotedblright} prompts intentionally designed to bypass safeguards built into deployed models. Lastly, we show that fine-tuning with data enriched with diverse perspectives can boost the reasoning capabilities of the model compared to fine-tuning with raw data alone.
SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs (2025)

Intent prediction in information-seeking dialogs is challenging and requires a substantial amount of data with human-labeled intents for effective model training. While Large Language Models (LLMs) have demonstrated effectiveness in generating synthetic data, existing methods typically rely on human feedback and are tailored to structured, task-oriented intents. In this paper, we leverage LLMs for zero-shot generation of large-scale, open-domain, intent-aware information-seeking dialogs to serve as training data for intent prediction models. We introduce SOLID, a method that generates dialogs turn by turn using novel self-seeding and multi-intent self-instructing strategies. Additionally, we propose SOLID-RL, a finetuned version that generates an entire dialog in one step using data created with SOLID. SOLID and SOLID-RL are each used to generate over 300k intent-aware dialogs, significantly surpassing the size of existing datasets. Experiments show that intent prediction models trained on sampled dialogs generated by SOLID and SOLID-RL outperform those trained solely on human-generated dialogs. Our findings demonstrate the potential of LLMs to expand training datasets, as they provide valuable resources for conversational agents across multiple tasks. Our self-seeding and self-instructing approaches are adaptable to various conversational data types and languages with minimal modifications.
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification (2025)

We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of syntactic-ngrams, enabling us to capture a complementary source of supervision. These rules alone lead to performance gains in both, in-context learning (ICL) and fine-tuning (FT) settings. Similarly, use of augmented data from ARISE alone improves the performance for a model, outperforming configurations that rely on complex methods like contrastive learning. Further, our extensive experiments on various datasets covering three full-shot, eight few-shot and seven multilingual variant settings demonstrate that the rules and data we generate lead to performance improvements across these diverse domains and languages.
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context (2025)

Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the absolute position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs' performance is also sensitive to the order, relative position, in which the supporting documents are presented. We refer to this as the \textbf{misordered context} problem. To address this issue, based on the theoretical approach, we propose a simple yet effective method called \textbf{co}ntext \textbf{re}petition (\textbf{CoRe}), which involves prompting the model by repeatedly presenting the context. This ensures that certain contiguous reasoning segments within supporting documents are presented in the optimal order, effectively guiding the model`s reasoning in the appropriate direction. Applying CoRe, we improve the F1 score by up to 30{\%}p on multi-hop QA tasks and increase accuracy by up to 70{\%}p on a synthetic task. Additionally, CoRe helps mitigate the well-known {\textquotedblleft}lost-in-the-middle{\textquotedblright} problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chain-of-Thought (CoT) reasoning.
Text Annotation via Inductive Coding: Comparing Human Experts to LLMs in Qualitative Data Analysis (2025)

This paper investigates the automation of qualitative data analysis, focusing on inductive coding using large language models (LLMs). Unlike traditional approaches that rely on deductive methods with predefined labels, this research investigates the inductive process where labels emerge from the data. The study evaluates the performance of six open-source LLMs compared to human experts. As part of the evaluation, experts rated the perceived difficulty of the quotes they coded. The results reveal a peculiar dichotomy: human coders consistently perform well when labeling complex sentences but struggle with simpler ones, while LLMs exhibit the opposite trend. Additionally, the study explores systematic deviations in both human and LLM-generated labels by comparing them to the golden standard from the test set. While human annotations may sometimes differ from the golden standard, they are often rated more favorably by other humans. In contrast, some LLMs demonstrate closer alignment with the true labels but receive lower evaluations from experts.