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Multi-hop question answering is widely used to evaluate the reasoning capabilities of large language models (LLMs), as it requires integrating multiple pieces of supporting knowledge to arrive at a correct answer. While prior work has compared fine-tuning and retrieval-augmented generation (RAG) for factual recall and single-hop question answering, it remains unclear how these approaches perform in multi-hop settings that require compositional reasoning over temporally novel knowledge. In particular, prior comparisons often do not control for model scale, evaluation format, or knowledge freshness, making it difficult to isolate the effect of knowledge injection mechanisms. In this paper, we systematically compare parametric and non-parametric knowledge injection methods for open-domain multi-hop question answering. We evaluate unsupervised fine-tuning (continual pretraining), supervised fine-tuning, and retrieval-augmented generation across three 7B-parameter open-source LLMs. Experiments are conducted on two benchmarks: Question Answering Science Challenge (QASC), a standard multi-hop science question answering dataset, and a newly constructed dataset of over 10,000 multi-hop questions derived from Wikipedia events in 2024, which is designed to test knowledge beyond the models’ pretraining cutoff. Our results show that unsupervised fine-tuning provides only limited gains over base models, suggesting that continual pretraining alone is insufficient for improving multi-hop reasoning accuracy. In contrast, RAG yields substantial and consistent improvements, particularly when answering questions that rely on temporally novel information. Supervised fine-tuning achieves the highest overall accuracy across models and datasets. These findings highlight fundamental differences in how knowledge injection mechanisms support multi-hop question answering and underscore the importance of retrieval-based methods when external or compositional knowledge is required.

ACL 2026

Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge

post-training

multi-hop question answering

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they apply it to clinically salient structured judgments. Here, we present a knowledge-graph (KG)-grounded benchmark for assessing LLMs on mental-health entity recognition, relation judgment, and two-hop reasoning. The benchmark is derived from PrimeKG and comprises nine task families with KG-supported answers and controlled negative options. Experiments across 15 closed- and open-source LLMs reveal a persistent recognition-to-judgment gap: leading models achieve near-ceiling performance on entity typing and on the small relation-typing subset, yet they still struggle with relation prediction and two-hop reasoning. Additionally, short KG-derived snippets benefit some models but degrade performance for others. Moreover, output-format reliability can substantially influence measured performance under constrained multiple-choice settings, highlighting the critical role of response validity in benchmark-based evaluation. MHGraphBench should therefore be interpreted as evaluating agreement with a curated mental-health slice of PrimeKG under a constrained multiple-choice interface, rather than as a direct assessment of real-world clinical safety.

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

Recent advancements in text-to-image generative models have improved narrative consistency in story visualization. However, current story visualization models often overlook cultural dimensions, resulting in visuals that lack cultural fidelity. In this study, we present a progressive evaluation framework for story visualization. We validate this framework on current text-to-image models across three languages (English, Hindi, and Chinese) on two datasets (VIST and FlintstonesSV). The proposed framework introduces three levels of cultural analysis as evaluation rubrics: 1) Basic Cultural Criteria, 2) Cultural Dimension Guidance, and 3) Cultural Examples Grounding. We evaluate story visualization by use of a novel MLLM-as-Jury approach across all three rubrics and a small-scale human evaluation only on the third rubric. We implement an MLLM-as-jury approach by aggregating scores from three different families of MLLM-as-Judge models. In our experiments, real-world stories generally receive higher cultural appropriateness scores than animated ones, with English tending to score higher than Hindi and Chinese across the evaluated models. Some examples also exhibited culturally inconsistent or stereotypical elements noted by annotators. The proposed progressive evaluation framework has therefore been shown to provide early insights into cultural misalignments in story visualization. Code for this work is made available on https://github.com/janak11111/Cultural_Eval_For_StoryViz

A Progressive Evaluation Framework for Multicultural Analysis of Story Visualization

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19%-53% reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems.

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

Automatic evaluation in industrial settings requires models to interpret and apply natural language rubrics reliably under language and domain shift. This challenge is compounded when reference answers are unavailable and proprietary models cannot be deployed due to data-governance constraints. We present E-Star-12B, a 12B-parameter evaluator for Korean industrial environments that jointly addresses rubric following and domain adaptation. Our approach combines a structured evaluation format—feedback, highlight, and decision—with a 6K high-confidence training set via multi-stage consensus-based filtering. We introduce two benchmarks: Ko Feedback Bench for rubric-following evaluation under Korean language transfer, and RAG Quality Bench for domain-specific evaluation in financial and legal settings. E-Star-12B achieves the strongest rubric alignment among small language models on Ko Feedback Bench, improving Pearson correlation by +0.173 over its base model. On RAG Quality Bench, the domain-adapted variant approaches frontier-model performance with more stable adaptation than general instruct models. Strong rubric-following capability serves as a reliable scaffold for subsequent domain adaptation.

E-star 12B: Reliable Rubric-Following and Domain-Adaptive SLM Evaluator for Korean Industrial Settings

Linear probes trained on internal activations of Large Language Models (LLMs) are increasingly proposed as evaluation metrics for deceptive generation, automated monitors that score whether a model's output was produced deceptively, without requiring ground-truth labels or human annotation. Yet these metrics report AUROC scores exceeding 0.96 on clean benchmarks while demonstrating profound fragility under distributional shift. This paper presents a systematic pressure-test of such probe-based evaluation metrics across the Gemma 3 model family (1B–27B parameters), diagnosing why they fail rather than merely documenting that they fail. We investigate four competing hypotheses about how deception is encoded: as (1) a single linear direction, (2) a multi-dimensional subspace, (3) a convex conic hull, or (4) a proxy for computational entropy. Our experimental design includes cross-domain transfer matrices, multi-dimensional probe analysis with permutation null baselines, entropy-residualization tests, and systematic distractor evaluations across 8 stylistic shifts. Across all four model scales, we find that: (a) probe-based metrics achieve near-perfect AUROC ($\ge$0.998) on clean data but collapse under stylistic shifts when trained without stylistic augmentation, style-augmented probes recover near-perfect detection (mean AUROC~0.979--0.983) even on unseen styles; (b) the single-direction hypothesis is decisively rejected (k=1 captures only 0.61–0.80 AUROC of the signal, with cross-domain transfer failure confirmed as geometric rather than layer-mismatch-driven; (c) the entropy-proxy hypothesis is rejected (maximum $|\rho|=0.454$, maximum $\Delta$AUROC after residualization=0.004); and (d) deception does not form a statistically significant linear subspace even within individual domains (per-domain $k^*{=}0$), yet multi-dimensional probes (k$\ge$5) consistently recover the signal through distributed sub-threshold features. These findings demonstrate that probe fragility under standard training reflects distributional narrowness rather than a fundamental architectural limitation: style-augmented probes recover near-perfect detection (mean AUROC 0.979–0.983 on unseen styles) at both the 4B and 27B scales, establishing that the inverse scaling pattern observed under standard training is a training-distribution artifact rather than a genuine scale-dependent phenomenon.

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations

Automated evaluation of student responses has developed along two largely separate tracks: Automated Essay Scoring (AES), which emphasizes holistic writing quality, and Automatic Short Answer Grading (ASAG), which emphasizes factual correctness and rubric alignment. We introduce S-GRADES, an open-source benchmark consolidating 14 AES and ASAG datasets (37K+ responses) with a standardized evaluation framework, and use it to systematically evaluate three frontier LLMs across six reasoning configurations. Our results show that hybrid prompting generally performs best, AES is more predictable than ASAG, and cross-dataset exemplar transfer is often unstable, especially across paradigms. These findings highlight the need for unified, benchmark-driven evaluation of reliability and generalization in LLM-based student assessment. This work is published at LREC-2026 and available on arXiv (https://arxiv.org/abs/2603.10233). The benchmark is publicly accessible at https://sgrades.eng.unt.edu.

S-GRADES - Studying Generalization of Student Response Assessments in Diverse Evaluative Settings

LLM-as-Judge systems are increasingly used to generate labels and evaluate conversational data, yet their susceptibility to narrative framing remains underexplored. We study whether replacing one speaker’s username with the first-person identifier 'Me' systematically biases model judgments independent of the underlying evidence. Using the Conversations Gone Awry corpus, we evaluate four LLMs across three judgment tasks (attack detection, attacker identification, and blame attribution), three perspective conditions, and two evidence visibility settings. Our results show that narrative perspective induces strong, task-dependent distortions, particularly in more subjective judgment tasks. We find that models systematically favor the narrator when a speaker is presented as 'Me', reducing blame and responsibility attribution toward that speaker even when the underlying evidence is unchanged. These findings raise concerns about using LLMs to judge or moderate first-person conversational data.

Sycophancy Negatively Affects LLM-as-a-Judge in Conflict Evaluation

Evaluating multimodal generations is challenging: human evaluation is costly, and single-model LLM-as-a-judge pipelines can be brittle and provide limited uncertainty signals. We introduce Concord, an ensemble-based evaluation pipeline that aggregates discrete judgments from multiple LLM judges and uses inter-judge agreement as a practical uncertainty signal for disagreement-driven triage. We evaluate Concord on AVSSD and SCORE-AVS, a ground-truth-supervised audio-visual benchmark with discrete labels (True/False or 0–5). Concord improves agreement with human judgments over single-judge and naive aggregation baselines, and prioritizing low-agreement instances focuses human review on the most ambiguous cases. We use locally hosted open-source judges and include the binary results for online larger scale models GPT4.o mini turbo and Gemini 3.1 Flash Lite.

Concord: An Agreement-Aware Multi-Adjudication Pipeline for LLM Evaluation

Large Language Models (LLMs) are increasingly used to support scientific research, but their knowledge of scientific advancements can quickly become outdated. We introduce ScienceMeter, a new framework for evaluating scientific knowledge update methods over scientific knowledge spanning the past, present, and future. ScienceMeter defines three metrics: knowledge preservation, the extent to which models' understanding of previously learned papers are preserved; knowledge acquisition, how well scientific claims from newly introduced papers are acquired; and knowledge projection, the ability of the updated model to anticipate or generalize to related scientific claims that may emerge in the future. Using ScienceMeter, we examine the scientific knowledge of LLMs on claim judgment and generation tasks across a curated dataset across ten domains. We evaluate five representative knowledge update approaches and find that the best-performing knowledge update methods can preserve only 85.9% of existing knowledge, acquire 71.7% of new knowledge, and project 37.7% of future knowledge, underscoring that developing robust scientific knowledge update mechanisms is both crucial and challenging.

ScienceMeter: Tracking Scientific Knowledge Updates in Language Models

Large Language Models are increasingly used as zero-shot classifiers in complex reasoning tasks. However, standard constrained decoding suffers from a phenomenon we define as Renormalization Bias. When a model is restricted to a small set of target labels, the standard softmax operation discards the probability mass assigned to semantic synonyms in the original distribution. This loss of information, which we call the Silent Vote, results in artificial overconfidence and poor calibration. We propose Semantic Softmax, an inference-time layer that recovers this lost information by aggregating the scores of the semantic neighborhood surrounding each target label. We evaluate this approach on Qwen-3 and Phi-4-mini models using GoEmotions and Civil Comments datasets. Our results demonstrate consistent improvements across all evaluation metrics: Semantic Softmax substantially reduces Expected Calibration Error (ECE) and Brier Score, while simultaneously enhancing discriminative performance in terms of AUROC and Macro-F1. By accounting for linguistic nuances, our method provides a more calibrated and accurate alternative for zero-shot classification.

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