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20242026
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cs.CL2026

SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series

Galann Pennec, Zhengyuan Liu, Nicholas Asher +2

We introduce SagaQA, a long-form video benchmark for multi-hop reasoning over full-length TV series. Existing video reasoning benchmarks often emphasize local understanding of adja…

cs.CL2026

The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation

Herun Wan, Jiaying Wu, Minnan Luo +4

Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts an…

cs.CL2026

MeMo: Memory as a Model

Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6

Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications req…

cs.CL2025

What Makes a Good Natural Language Prompt?

Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen +4

As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However,…

cs.CL2025

Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines

Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong +5

In-context learning (ICL) is an important yet not fully understood ability of pre-trained large language models (LLMs). It can greatly enhance task performance using a few examples…

cs.CL2025

LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs

Do Xuan Long, Hai Nguyen Ngoc, Tiviatis Sim +5

We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation…