ymoslem/acl-abstracts huggingface dataset:
Investigating the Zone of Proximal Development of Language Models for In-Context Learning (2025)

In this paper, we introduce a learning analytics framework to analyze the in-context learning (ICL) behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. ZPD delineates the range of tasks a learner can accomplish with appropriate guidance but not yet independently. We adapt this concept to ICL, measuring the ZPD of LLMs based on model performance on individual examples in different settings. Furthermore, we propose an item response theory (IRT) model to predict the distribution of zones for LLMs. Our findings reveal a series of intricate and multifaceted behaviors of ICL, providing new insights into understanding and leveraging this technique. Finally, we demonstrate how our framework can enhance LLM in both inference and fine-tuning scenarios: (1) By predicting a model`s zone distribution, we selectively apply ICL to queries that are most likely to benefit from demonstrations, achieving a better balance between inference cost and performance; (2) We propose a human-like curriculum for fine-tuning, which prioritizes examples within the model`s ZPD. The curriculum results in improved performance, and we explain its effectiveness through an analysis of the training dynamics of LLMs.
Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In (2025)

Following the advancement of large language models (LLMs), the development of LLM-based autonomous agents has become prevalent.As a result, the need to understand the security vulnerabilities of these agents has become a critical task. We examine how ReAct agents can be exploited using a straightforward yet effective method we refer to as the foot-in-the-door attack.Our experiments show that indirect prompt injection attacks, prompted by harmless and unrelated requests (such as basic calculations) can significantly increase the likelihood of the agent performing subsequent malicious actions.Our results show that once a ReAct agent`s thought includes a specific tool or action, the likelihood of executing this tool in the subsequent steps increases significantly, as the agent seldom re-evaluates its actions. Consequently, even random, harmless requests can establish a {\textquoteleft}foot-in-the-door', allowing an attacker to embed malicious instructions into the agent`s thought process, making it more susceptible to harmful directives.To mitigate this vulnerability, we propose implementing a simple reflection mechanism that prompts the agent to reassess the safety of its actions during execution, which can help reduce the success of such attacks.
As easy as PIE: understanding when pruning causes language models to disagree (2025)

Language Model (LM) pruning compresses the model by removing weights, nodes, or other parts of its architecture. Typically, pruning focuses on the resulting efficiency gains at the cost of effectiveness.However, when looking at how individual data pointsare affected by pruning, it turns out that a particular subset of data points always bears most of the brunt (in terms of reduced accuracy) when pruning,but this effect goes unnoticed when reporting the mean accuracy of all data points. These data points are called PIEs and have been studied in image processing, but not in NLP.In a study of various NLP datasets, pruning methods, and levels of compression, we find that PIEs impact inference quality considerably, regardless of class frequency, andthat BERT is more prone to this than BiLSTM. We also find that PIEs contain a high amount of data points that have the largest influence on how well the model generalises to unseen data. This means that when pruning, with seemingly moderate loss to accuracy across all data points, we in fact hurt tremendously those data points that matter the most. We trace what makes PIEs both hard and impactful to inference to their overall longer and more semantically complex text. These findings are novel and contribute to understanding how LMs are affected by pruning. The code is available at: https://github.com/pietrotrope/AsEasyAsPIE
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction (2025)

Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Leveraging real-legal case sources, MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants' characters and behaviors as well as addressing distractions. A Multi-stage Interactive Legal Evaluation (MILE) benchmark is further constructed to evaluate LLMs' performance in dynamic legal scenarios. Extensive experiments confirm the effectiveness of our framework.
Exploring Backward Reasoning in Large Language Models (2025)

Multi-step reasoning through in-context learning strategies have been extensively explored, highlighting the abilities of Large Language Models (LLMs) to generate answers derived from step-by-step reasoning. These studies focus the attention on LLMs' forward reasoning abilities epitomised in a series of general premises leading to a final solution. In this paper, by taking the reverse perspective, we study the backward reasoning abilities of LLMs, namely the inference that leads to the causal hypothesis. Behind formalising the backward problems, we analyse whether the LLMs are able to reason about the conclusion and reconstruct the original question that led to the delivery of the final answer. Operating with question-answering tasks involving symbolic reasoning, understanding, and commonsense abilities, we observe that the proposed models reveal robust comprehension capabilities managing different kinds of input; however, they are not always able to reason in the backward direction. Finally, to challenge this limitation, we demonstrate that instructing LLMs to generate the answer by reconsidering the structure of the problem allows for improved backward reasoning direction.
MMLF: Multi-query Multi-passage Late Fusion Retrieval (2025)

Leveraging large language models (LLMs) for query expansion has proven highly effective across diverse tasks and languages. Yet, challenges remain in optimizing query formatting and prompting, often with less focus on handling retrieval results. In this paper, we introduce Multi-query Multi-passage Late Fusion (MMLF), a straightforward yet potent pipeline that generates sub-queries, expands them into pseudo-documents, retrieves them individually, and aggregates results using reciprocal rank fusion. Our experiments demonstrate that MMLF exhibits superior performance across five BEIR benchmark datasets, achieving an average improvement of 4{\%} and a maximum gain of up to 8{\%} in both Recall@1k and nDCG@10 compared to state of the art across BEIR information retrieval datasets.
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025)

With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key issues: (i) inadequate defense capabilities, particularly in domain-specific scenarios like chemistry, where a lack of specialized knowledge can lead to the generation of harmful responses to malicious queries. (ii) over-defensiveness, which compromises the general utility and responsiveness of LLMs. To mitigate these issues, we introduce a multi-agents-based defense framework, Guide for Defense (G4D), which leverages accurate external information to provide an unbiased summary of user intentions and analytically grounded safety response guidance. Extensive experiments on popular jailbreak attacks and benign datasets show that our G4D can enhance LLM`s robustness against jailbreak attacks on general and domain-specific scenarios without compromising the model`s general functionality.
kNN For Whisper And Its Effect On Bias And Speaker Adaptation (2025)

Speech recognition performance varies by language, domain, and speaker characteristics such as accent, but fine-tuning a model on any of these categories may lead to catastrophic forgetting. Token-level $k$ nearest neighbor search ($k$NN), first proposed for neural sequence decoders for natural language generation (NLG) and machine translation (MT), is a non-parametric method that instead adapts using inference-time search in an external datastore, without training the underlying model. We show that Whisper, a transformer end-to-end speech model, benefits from $k$NN. We investigate the differences between the speech and text setups. We discuss implications for speaker adaptation, and analyze improvements by gender, accent, and age.
VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning (2025)

Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static syntax, they often struggle with dynamic reasoning tasks. We introduce VisualCoder, a simple yet effective approach that enhances code reasoning by integrating multimodal Chain-of-Thought (CoT) reasoning with a visual Control Flow Graph (CFG). By aligning code snippets with their corresponding CFGs, VisualCoder provides deeper insights into execution flows. We address challenges in multimodal CoT integration through a reference mechanism, ensuring consistency between code and its execution path, thereby improving performance in program behavior prediction, error detection, and output generation.
Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation (2025)

The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not optimized for non-English languages, leading to inefficient encoding (high token {\textquotedblleft}fertility{\textquotedblright}) and slower inference speed.In this work, we thoroughly compare a variety of vocabulary adaptation techniques for optimizing English LLMs for the Italian language, and put forward Semantic Alignment Vocabulary Adaptation (SAVA), a novel method that leverages neural mapping for vocabulary substitution. SAVA achieves competitive performance across multiple downstream tasks, enhancing grounded alignment strategies. We adapt two LLMs: Mistral-7B-v0.1, reducing token fertility by 25{\%}, and Llama-3.1-8B, optimizing the vocabulary and reducing the number of parameters by 1 billion. We show that, following the adaptation of the vocabulary, these models can recover their performance with a relatively limited stage of continual training on the target language. Finally, we test the capabilities of the adapted models on various multi-choice and generative tasks.
Beyond the Mode: Sequence-Level Distillation of Multilingual Translation Models for Low-Resource Language Pairs (2025)

This paper delves into sequence-level knowledge distillation (KD) of multilingual pre-trained translation models. We posit that, beyond the approximated mode obtained via beam search, the whole output distribution of the teacher contains valuable insights for students. We explore the potential of n-best lists from beam search to guide student`s learning and then investigate alternative decoding methods to address observed issues like low variability and under-representation of infrequent tokens. Our research in data-limited scenarios reveals that although sampling methods can slightly compromise the translation quality of the teacher output compared to beam search based methods, they enrich the generated corpora with increased variability and lexical richness, ultimately enhancing student model performance and reducing the gender bias amplification commonly associated with KD.
LLMs for Extremely Low-Resource Finno-Ugric Languages (2025)

The advancement of large language models (LLMs) has predominantly focused on high-resource languages, leaving low-resource languages, such as those in the Finno-Ugric family, significantly underrepresented. This paper addresses this gap by focusing on V{\~o}ro, Livonian, and Komi. We cover almost the entire cycle of LLM creation, from data collection to instruction tuning and evaluation. Our contributions include developing multilingual base and instruction-tuned models; creating evaluation benchmarks, including the smugri-MT-bench multi-turn conversational benchmark; and conducting human evaluation. We intend for this work to promote linguistic diversity, ensuring that lesser-resourced languages can benefit from advancements in NLP.
LOFT: Scalable and More Realistic Long-Context Evaluation (2025)

Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveraging LCLMs' ability to natively ingest and process entire corpora of information offers numerous advantages. It enhances user-friendliness by eliminating the need for specialized knowledge of tools, provides robust end-to-end modeling that minimizes cascading errors in complex pipelines, and allows for the application of sophisticated prompting techniques across the entire system. To assess this paradigm shift, we introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks. However, LCLMs still face challenges in areas like compositional reasoning that are required in SQL-like tasks. Notably, prompting strategies significantly influence performance, emphasizing the need for continued research. Overall, LOFT provides a rigorous testing ground for LCLMs, showcasing their capabilities to tackle existing paradigms.
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems (2025)

Retrieval-augmented generation (RAG) has emerged as an approach to augment large language models (LLMs) by reducing their reliance on static knowledge and improving answer factuality. RAG retrieves relevant context snippets and generates an answer based on them. Despite its increasing industrial adoption, systematic exploration of RAG components is lacking, particularly regarding the ideal size of provided context, and the choice of base LLM and retrieval method. To help guide development of robust RAG systems, we evaluate various context sizes, BM25 and semantic search as retrievers, and eight base LLMs. Moving away from the usual RAG evaluation with short answers, we explore the more challenging long-form question answering in two domains, where a good answer has to utilize the entire context. Our findings indicate that final QA performance improves steadily with up to 15 snippets but stagnates or declines beyond that. Finally, we show that different general-purpose LLMs excel in the biomedical domain than the encyclopedic one, and that open-domain evidence retrieval in large corpora is challenging.
Aligning Black-box Language Models with Human Judgments (2025)

Large language models (LLMs) are increasingly used as automated judges to evaluate recommendation systems, search engines, and other subjective tasks, where relying on human evaluators can be costly, time-consuming, and unscalable. LLMs offer an efficient solution for continuous, automated evaluation. However, since the systems that are built and improved with these judgments are ultimately designed for human use, it is crucial that LLM judgments align closely with human evaluators to ensure such systems remain human-centered. On the other hand, aligning LLM judgments with human evaluators is challenging due to individual variability and biases in human judgments. We propose a simple yet effective framework to align LLM judgments with individual human evaluators or their aggregated judgments, without retraining or fine-tuning the LLM. Our approach learns a linear mapping between the LLM`s outputs and human judgments, achieving over 142{\%} average improvement in agreement across 29 tasks with only a small number of calibration examples used for training. Notably, our method works in zero-shot and few-shot settings, exceeds inter-human agreement on four out of six tasks, and enables smaller LLMs to achieve performance comparable to that of larger models.
Guideline Compliance in Task-Oriented Dialogue: The Chained Prior Approach (2025)

Task-oriented dialogue (TOD) systems are widely used across various domains, including customer service, appointment scheduling, and technical support. In real-world scenarios, such systems must adhere to given operational guidelines. However, existing solutions based on large language models often cannot achieve strict guideline compliance, even when fine-tuned with domain knowledge. To address this issue, we introduce a novel TOD system named GuidedTOD, which explicitly considers domain-specific guidelines by integrating a policy module. This module employs a Markov Chain, termed Chained Prior, to efficiently encode and dynamically update guideline knowledge. During inference, the Chained Prior re-ranks outputs from the domain-expert language model using beam search, ensuring guideline adherence. Experimental results show that GuidedTOD significantly improves guideline compliance, achieving approximately 20{\%} better action prediction accuracy than state-of-the-art solutions. Code is available here: https://github.com/cure-lab/GuidedTOD.
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization via Multi-LLMs (2025)

Recent studies show that large language models (LLMs) are vulnerable to jailbreak attacks, which can bypass their defense mechanisms. However, existing jailbreak research often exhibits limitations in universality, validity, and efficiency. Therefore, we rethink jailbreaking LLMs and define three key properties to guide the design of effective jailbreak methods. We introduce AutoBreach, a novel black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts. By leveraging LLMs' summarization and reasoning abilities, AutoBreach minimizes manual effort. To boost jailbreak success rates, we further suggest sentence compression and chain-of-thought-based mapping rules to correct errors and wordplay misinterpretations in target LLMs. Also, we propose a two-stage mapping rule optimization that initially optimizes mapping rules before querying target LLMs to enhance efficiency. Experimental results indicate AutoBreach efficiently identifies security vulnerabilities across various LLMs (Claude-3, GPT-4, etc.), achieving an average success rate of over 80{\%} with fewer than 10 queries. Notably, the adversarial prompts generated by AutoBreach for GPT-4 can directly bypass the defenses of the advanced commercial LLM GPT o1-preview, demonstrating strong transferability and universality.
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025)

In this study, we introduce \textbf{BanNERD}, the most extensive human-annotated and validated \textbf{B}angla \textbf{N}amed \textbf{E}ntity \textbf{R}ecognition \textbf{D}ataset to date, comprising over 85,000 sentences. BanNERD is curated from a diverse array of sources, spanning over 29 domains, thereby offering a comprehensive range of generalized contexts. To ensure the dataset`s quality, expert linguists developed a detailed annotation guideline tailored to the Bangla language. All annotations underwent rigorous validation by a team of validators, with final labels being determined via majority voting, thereby ensuring the highest annotation quality and a high IAA score of 0.88. In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets. Additionally, we propose a method named \textit{BanNERCEM} (Bangla NER context-ensemble Method) which outperforms existing approaches on Bangla NER datasets and performs competitively on English datasets using lightweight Bangla pretrained LLMs. Our approach passes each context separately to the model instead of previous concatenation-based approaches achieving the highest average macro F1 score of 81.85{\%} across 10 NER classes, outperforming previous approaches and ensuring better context utilization. We are making the code and datasets publicly available at \url{https://github.com/eblict-gigatech/BanNERD} in order to contribute to the further advancement of Bangla NLP.
Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias (2025)

Citation practices are crucial in shaping the structure of scientific knowledge, yet they are often influenced by contemporary norms and biases. The emergence of Large Language Models (LLMs) introduces a new dynamic to these practices. Interestingly, the characteristics and potential biases of references recommended by LLMs that entirely rely on their parametric knowledge, and not on search or retrieval-augmented generation, remain unexplored. Here, we analyze these characteristics in an experiment using a dataset from AAAI, NeurIPS, ICML, and ICLR, published after GPT-4`s knowledge cut-off date. In our experiment, LLMs are tasked with suggesting scholarly references for the anonymized in-text citations within these papers. Our findings reveal a remarkable similarity between human and LLM citation patterns, but with a more pronounced high citation bias, which persists even after controlling for publication year, title length, number of authors, and venue. The results hold for both GPT-4, and the more capable models GPT-4o and Claude 3.5 where the papers are part of the training data. Additionally, we observe a large consistency between the characteristics of LLM`s existing and non-existent generated references, indicating the model`s internalization of citation patterns. By analyzing citation graphs, we show that the references recommended are embedded in the relevant citation context, suggesting an even deeper conceptual internalization of the citation networks. While LLMs can aid in citation generation, they may also amplify existing biases, such as the Matthew effect, and introduce new ones, potentially skewing scientific knowledge dissemination.
What can Large Language Models Capture about Code Functional Equivalence? (2025)

Code-LLMs, LLMs pre-trained on large code corpora, have shown great progress in learning rich representations of the structure and syntax of code, successfully using it to generate or classify code fragments. At the same time, understanding if they are able to do so because they capture code semantics, and how well, is still an open question. In this paper, we tackle this problem by introducing SeqCoBench, a benchmark for systematically assessing how Code-LLMs can capture code functional equivalence. SeqCoBench contains over 20 code transformations that either preserve or alter the semantics of Python programs. We conduct extensive evaluations in different settings, including zero-shot and parameter-efficient finetuning methods on state-of-the-art (Code)-LLMs to see if they can discern semantically equivalent or different pairs of programs in SeqCoBench. We find that the performance gap between these LLMs and classical match-based retrieval scores is minimal, with both approaches showing a concerning lack of depth in understanding code semantics.