cs.CL · 2026-08-11 · No. 81
Computation and Language, 2026-08-11.
16 new papers in cs.CL. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
16 entries-
01
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
Laurens Samson, Iva Gornishka, Gossa Lô, Yuki M. Asano, Sennay Ghebreab
cs.CL · cs.AI
Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of public administration and the linguistic requirements of non-English contexts. We present the "Grip on LLMs" framework, a systematic evaluation suite for Dutch governmental use developed in collaboration with domain experts from a major Dutch municipal organisation. Through an advisory board...
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02
Consilience for Verifier-Free Test-Time Scaling
Lecheng Kong, Like Hui, Haitao Mao, Jun Huan
cs.CL · cs.LG
Test-time scaling often uses an external verifier, such as compilers and test cases in coding or trained value functions in robotics applications, to obtain high-quality rollouts. Verifier-free test-time scaling (or VF-TTS) is gaining extensive attention as a mechanism to enhance Large Language Model (LLM) reasoning, primarily because we do not have access to such high-quality verifiers in many real-world applications. Among existing VF-TTS...
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03
Fusion Training for Mathematical Generalization in Large Language Models
Congfeng Cao, Pengyu Zhang, Jelke Bloem
cs.CL · cs.AI
Thinking Mode Fusion (TMF) enables large language models to support both concise responses and long-form reasoning by unifying a non-thinking mode and a thinking mode within a single model. However, its training dynamics, including the \emph{data ratio} and \emph{training schedule} between the two modes, remain underexplored. In this work, we present a systematic study of TMF by analyzing the effects of the training schedule and data ratio...
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04
RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification
Fan Zhang, Jiaming Li
cs.CL · cs.LG
Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making. Existing work on resource-efficient financial NLP has largely focused on compressing or adapting pretrained language models, with less attention to combining contextual representations with lightweight rule-derived features. This study develops Rule-Aware FinBERT (RA-FinBERT), a parameter-efficient...
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05
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
Ghanshyam Verma, Simanta Sarkar, Devishree Pillai, Hotaka Shiokawa, Yourong Xu, Fiona Veazey, Peter Hubbert, Hui Su,...
cs.CL · cs.AI
Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts where general-purpose LLMs and RAG tend to underperform. We hypothesize that augmenting RAG with unstructured and structured knowledge, extracted from both documents and knowledge graphs (KGs), can improve reasoning and answer accuracy for such tasks. To test this, we...
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06
Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness
Pinzhen Chen, Koel Dutta Chowdhury, Xiaoya Xu, David Tan, Doreen Osmelak, Ona de Gibert, Ariun-Erdene Tumurchuluun,...
cs.CL · cs.AI
Multilingual translation benchmarks are typically sourced in English and translated into other languages, treating language pairs as the unit of evaluation---a design that is prone to contamination over time and overlooks locale and cultural considerations. We therefore advocate for source-contrastive evaluation and instantiate it with Cultivar, a localised subset of FLORES, which enables locale-specific translation evaluation. When paired...
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07
How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans
Hasan Mahmud, Khawaja Abaid Ullah, Mohammad Javad Khojasteh, Jamison Heard, Prabu David
cs.CL · cs.AI · cs.CY
Large language models (LLMs) are increasingly used to perform subjective evaluations traditionally made by humans, yet their validity as social judges remains unclear. This paper examines whether LLMs can assess social attraction from theory-grounded persona profiles constructed from ten psychological and relational constructs and organized into three tiers: socially attractive, socially mixed, and socially unattractive. We examine LLM...
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08
Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks
Mingyu Luo, Ming Deng, Zilang Qiu, Yiming Cheng, Ci Tao, Xue Tan, Sijin Sun, Yangfu Li, Ping Chen, Jun Dai, Xiaoyan Sun
cs.CL · cs.AI · cs.CR
Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt. Jailbreak success is an outcome produced later by a particular target model, decoding policy, and judge. A filter tuned on a score that measures the...
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09
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
Yilin Jiang, Xiaorong Zhu, Fei Tan, Zicheng Zhang, Kaiyi Huang, Yang Yu, Zexuan Fei, Yiming Luo, Keqian Li, Hao Hao,...
cs.CL · cs.AI · cs.CY
Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators. These roles place demands that ordinary question answering does not: a usable education-facing model is supposed to be accurate, safe under sensitive prompts, instructionally useful, and aligned with pedagogical goals at the same time. Existing benchmarks evaluate these requirements largely in isolation, so none assesses...
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10
TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability
Vincent Cohen-Addad, Dimitris Paparas, Ernest van Wijland, Max Springer, Julien Canitrot-Paradis, Honghao Lin, David...
cs.CL · cs.AI
We introduce TCS-Bench, a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation. TCS-Bench consists of theorem-proving tasks from papers published at top theoretical computer science venues (STOC, FOCS, and SODA). Each task provides the necessary context to derive a self-contained proof for a target result. We evaluate state-of-the-art models on this benchmark. We verify...
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11
Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts
Kevin Thomas, Milosz Kasprzyk, Reuel C Igbokwe Onuigbo, Elliott Pert, Cameron Tovey, João A. Leite, Olesya...
cs.CL · cs.AI · cs.SI
Detecting machine-generated disinformation on social media is increasingly difficult as large language models (LLMs) make it easier to generate and rewrite misleading content at scale. Static benchmark evaluations, measuring detector performance on fixed held-out datasets, do not capture how detectors behave when posts are deliberately transformed to evade classification. This paper adapts the Build it, Break it, Fix it framework into Build...
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12
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo
cs.CL · cs.AI
Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a particular downstream task. Directly supplying the full preference summary therefore wastes context capacity and introduces cross-task distraction, while manually designing task-specific preference views is difficult to scale. In this work, we study \emph{task-specific...
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13
ZetaGPT: A Reference Implementation of Positional--Encoding--Free State--Space--Attention Language Models
Róisín Luo
cs.CL · cs.AI
Transformer-based language models rely on self-attention, whose computation is permutation-equivariant and therefore lacks an intrinsic mechanism for representing token order. Existing architectures address this limitation by explicitly incorporating positional information through learned positional embeddings or hand-crafted positional encodings, such as rotary positional encoding (RoPE), treating positional information as an architecturally...
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14
Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
Rose Cymbler, Daniel Guez, Laurent Fabre
cs.CL · cs.AI · cs.IR
We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the applicable version is an earlier or future one. Standard legal RAG treats the corpus as static; we argue legal question answering is a temporally-indexed retrieval problem. We introduce FiscalQA Pro, pairing a versioned corpus of 32,436 article-versions of the French tax code (93 years, 1938-2031)...
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15
Verifiably grounded machine interpretation of lunar geology
Tom Sander, Kay Wohlfarth, Christian Wöhler
cs.CL · cs.LG
Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations. Here, we present a step toward an automated "machine intelligence geologist" by embedding this distinct methodology of geologic knowledge discovery and inference into a multimodal vision-language architecture. Focusing on the stratigraphy of lunar basaltic mare volcanism, we train a model to generate verifiably grounded...
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16
UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers
Chidaksh Ravuru, Shashank Srivastava
cs.CL · cs.LG
Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs. Existing approaches either require manual specification of the feature vocabulary or automate discovery only partially, leaving the gap between dataset-level correlation and...
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