India

Translating natural language into formal language such as First-Order Logic (FOL) is a foundational challenge in NLP with wide-ranging applications in automated reasoning, misinformation tracking, and knowledge validation. In this paper, we introduce Natural Language to First-Order Logic (NL2FOL), a framework to autoformalize natural language to FOL step-by-step using Large Language Models (LLMs). Our approach addresses key challenges in this translation process, including the integration of implicit background knowledge. By leveraging structured representations generated by NL2FOL, we use Satisfiability Modulo Theory (SMT) solvers to reason about the logical validity of natural language statements. We present logical fallacy detection as a case study to evaluate the efficacy of NL2FOL. Being neurosymbolic, our approach also provides interpretable insights into the reasoning process and demonstrates robustness without requiring model fine-tuning or labeled training data. Our framework achieves good performance on multiple datasets--on the Logic dataset, NL2FOL achieves an F1-score of 78%, while generalizing effectively to the LogicClimate dataset with an F1-score of 80%.

IJCNLP-AACL 2025

Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection

logical fallacy detection

neurosymbolic nlp

autoformalization

symbolic reasoning

logical reasoning

interpretability

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
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The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge exists to deliver quantized models with minimal loss of accuracy in reasonable time, and in particular to do so without requiring mechanisms incompatible with specialized inference accelerators. Here, we demonstrate a simple, end-to-end quantization-aware training approach that, with an increase in total model training budget of less than 0.1%, outperforms the leading published quantization methods by large margins on several modern benchmarks, with both base and instruct model variants. The approach easily generalizes across different model architectures, can be applied to activations, cache, and weights, and requires the introduction of no additional operations to the model other than the quantization itself.

SiLQ: Simple Large Language Model Quantization-Aware Training

As large language models (LLMs) continue to scale, the memory footprint of Key-Value (KV) caches during inference has become a significant bottleneck. Existing approaches primarily focus on compressing KV caches within a single prompt or reusing shared prefixes or frequently occurred text segments across prompts. However, such strategies are limited in scenarios where prompts are semantically similar but lexically different, which frequently occurs in tasks such as multi-document summarization and conversational agents. We propose SemShareKV, a KV cache sharing and compression framework that accelerates LLM inference by reusing KV cache in semantically similar prompts. Instead of relying on exact token matches, SemShareKV applies fuzzy token matching using Locality-Sensitive Hashing (LSH) on token embeddings and incorporates Rotary Position Embedding (RoPE) to better preserve positional information. By selectively reusing relevant KV pairs from a reference prompt's cache, SemShareKV reduces redundant computation while maintaining output quality. Experiments on diverse summarization datasets show up to 6.25$\times$ speedup and 42\% lower GPU memory usage with 5k tokens input, with negligible quality degradation. These results highlight the potential of semantic-aware cache sharing for efficient LLM inference.

SemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching

Effective tool pre-selection via retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a Knowledge Graph (KG)-based tool retrieval framework that captures the semantic relationships between tools and their functional dependencies. Our retrieval algorithm leverages ensembles of 1-hop ego tool graphs to model direct and indirect connections between tools, enabling more comprehensive and contextual tool selection for multi-step tasks. We evaluate our approach on a synthetically generated internal dataset across six defined user classes, extending previous work on coherent dialogue synthesis and tool retrieval benchmarks. Results demonstrate that our tool graph-based method achieves 91.85% tool coverage on the micro-average CompleteRecall metric, compared to 89.26% for re-ranked semantic-lexical hybrid retrieval, the strongest non-KG baseline in our experiments. These findings support our hypothesis that the structural information modeled in the graph provides complementary signals to pure similarity matching, particularly for queries requiring sequential tool composition.

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning

Test-time compute has emerged as a powerful paradigm in function-level code generation. However, previous proposed strategies have been viewed as disparate, thus lacking a fair apples-to-apples analysis enabling understanding of their operational mechanisms in execution-based benchmarks. Therefore, we present a mathematical framework that unifies generation and reranking with theoretical justifications through the lens of Minimum Bayes Risk (MBR) decoding. Our proposed framework leads to key research questions regarding the effectiveness of using parallel and/or iterative sampling, design choices of reranking signals and soft/hard MBR utility functions, and behaviors of the final selected program across different methods. Our empirical findings highlight the importance of the diversity of sampled candidates (over self-improvement), reranking with simple and high-quality signals, and the effectiveness of test-time compute to select programs that manifest general and edge test case robustness. We will open-source our analysis toolkit and implementation to enable reproducible research.We open-source our analysis toolkit and implementation to enable reproducible research.

Formalizing Test-Time Compute for Function-Level Code Generation

The rise of generative AI has led to challenges in distinguishing AI-generated text from human-written content, raising concerns about misinformation and content authenticity. Detecting AI-generated text remains challenging, especially under various stylistic domains and paraphrased inputs. We introduce SGG-ATD, a novel detection framework that models structural and contextual relationships between LLM-predicted and original-input text. By masking parts of the input and reconstructing them using a language model, we capture implicit coherence patterns. These are encoded in a graph where cosine and contextual links between keywords guide classification via a Graph Convolutional Network (GCN). SGG-ATD achieves strong performance across diverse datasets and shows resilience to adversarial rephrasing and out-of-distribution inputs, outperforming competitive baselines.

Seeing Through the Mask: AI-Generated Text Detection with Similarity-Guided Graph Reasoning

Translating natural language requirements into Signal Temporal Logic (STL) is essential for safety-critical systems but requires mathematical expertise. We propose a translational grammar mapping Universal Dependencies (UD) structures to STL Operators through 17 theoretically-motivated patterns, evaluated on the NL2TL benchmarking dataset of 7,002 expert-annotated sentence-STL pairs, and an additional cross-domain analysis. We built a parser guided by this grammar to explore the formal deterministic relationship between UDR Compositions and STL Operators, achieving $\sim$99\% sentence coverage, $\sim$54\% exact matches (and $\sim$97\% similarity). Sentence-level regression analyses predict STL statements and STL Operator classes, considering the co-occurance of UDR substructures (UDR components) with an accuracy of more than $\sim$74\% and $\sim$81\%, respectively. They uncover a new logical grammatical link between temporal NL and formal logic, that is conditioned by the sentence-level context, and provide insights into how linguistic theory unfolds in practice through temporal linguistic expressions.

Merging Two Grammar Worlds: Exploring the Relationship between Universal Dependencies and Signal Temporal Logic

The development of large language models (LLMs) has resulted in significant transformations in the field of chemistry, with potential applications in molecular science. Traditionally, the exploration of methods to enhance pre-trained general-purpose LLMs has focused on techniques like supervised fine-tuning (SFT) and retrieval-augmented generation (RAG), to improve model performance and tailor them to specific applications. General purpose extended approaches are being researched, but their adaptation within the chemical domain has not progressed significantly. This study advances the application of LLMs in molecular science by exploring SFT of LLMs, and developing RAG and multimodal models, incorporating molecular embeddings derived from molecular fingerprints and other properties. Experimental results show that a multimodal model with fingerprint inputs to the LLM achieved the highest overall performance. For molecular representation based on SMILES notation, fingerprints effectively capture the structural information of molecular compounds, demonstrating the applicability of LLMs in drug discovery research.

Enhancing LLM-Based Molecular Captioning with Molecular Fingerprints

Large language models (LLMs) are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and literature summarization. However, their use in peer review remains prohibited due to concerns around confidentiality breaches, hallucinated content, and inconsistent evaluations. As LLM-generated text becomes more indistinguishable from human writing, there is a growing need for reliable attribution mechanisms to preserve the integrity of the review process. In this work, we evaluate topic-based watermarking (TBW), a semantic-aware technique designed to embed detectable signals into LLM-generated text. We conduct a systematic assessment across multiple LLM configurations, including base, few-shot, and fine-tuned variants, using authentic peer review data from academic conferences. Our results show that TBW maintains review quality relative to non-watermarked outputs, while demonstrating robust detection performance under paraphrasing. These findings highlight the viability of TBW as a minimally intrusive and practical solution for LLM attribution in peer review settings.

The Feasibility of Topic-Based Watermarking on Academic Peer Reviews

Discourse relation parsing plays a crucial role in uncovering the logical structure of text, yet existing corpora focus almost exclusively on general‐domain genres, leaving specialized fields like engineering under‐resourced. We introduce ENG‑DRB, the first PDTB‑style discourse relation corpus derived from transcripts of hands‑on engineering tutorial videos. ENG‑DRB comprises 11 tutorials spanning civil, mechanical, and electrical/electronics engineering (155 minutes total) with 1,215 annotated relations. Compared to general‑domain benchmarks, this dataset features a high proportion of explicit senses, dense causal and temporal relations, and frequent overlapping and embedded senses. Our benchmarking experiments underscore the dataset’s difficulty. A top parser (HITS) detects segment boundaries well (98.6\% F1), but its relation classification is more than 11 F1 percentages lower than on the standard PDTB. In addition, state‑of‑the‑art LLMs (OpenAI o4‑mini, Claude 3.7, LLaMA‑3.1) achieve at best 41\% F1 on explicit relations and less than 9\% F1 on implicit relations, revealing systematic errors in temporal and causal sense detection. The dataset can be accessed at: https://doi.org/10.57967/hf/6895. Code to reproduce our results is available at: https://github.com/chengzhangedu/ENG-DRB.

ENG-DRB: PDTB-style Discourse Relation Bank on Engineering Tutorial Video Scripts

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval with language generation. While early RAG systems based on static pipelines have shown effectiveness in well-structured tasks, they struggle in real-world scenarios requiring complex reasoning, dynamic retrieval, and multi-modal integration. To address these challenges, the field has shifted toward Reasoning Agentic RAG, a paradigm that embeds decision-making and adaptive tool use directly into the retrieval process. In this paper, we present a comprehensive review of Reasoning Agentic RAG methods, categorizing them into two primary systems: predefined reasoning, which follow fixed modular pipelines to boost reasoning, and agentic reasoning, where the model autonomously orchestrates tool interaction during inference. We analyze representative techniques under both paradigms, covering architectural design, reasoning strategies, and tool coordination. Finally, we discuss key research challenges and propose future directions to advance the flexibility, robustness, and applicability of reasoning agentic RAG systems.

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SiLQ: Simple Large Language Model Quantization-Aware Training

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