Idan Szpektor
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RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
Aviv Slobodkin
Hagai Taitelbaum
Brian Gordon
Michal Sokolik
Almog Gueta
Royi Rassin
Dani Lischinski
2025
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Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability - ranging from enhanced personalization in image generation to consistent character representation in video rendering - progress in this field is limited by the lack of reliable automatic evaluation. Existing methods either assess only one aspect of the task (i.e., textual alignment or subject preservation), misalign with human judgments, or rely on costly API-based evaluation. To address this gap, we introduce RefVNLI, a cost-effective metric that evaluates both textual alignment and subject preservation in a single run. Trained on a large-scale dataset derived from video-reasoning benchmarks and image perturbations, RefVNLI outperforms or statistically matches existing baselines across multiple benchmarks and subject categories (e.g., Animal, Object), achieving up to 6.4-point gains in textual alignment and 5.9-point gains in subject preservation.
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A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-reliance and eroded trust. We present the first systematic study of faithful confidence calibration of LLMs, benchmarking models' ability to use linguistic expressions of uncertainty that faithfully reflect their intrinsic uncertainty, across a comprehensive array of models, datasets, and prompting strategies. Our results demonstrate that LLMs largely fail at this task, and that existing interventions are insufficient: standard prompt approaches provide only marginal gains, and existing, factuality-based calibration techniques can even harm faithful calibration. To address this critical gap, we introduce MetaFaith, a novel prompt-based calibration approach inspired by human metacognition. We show that MetaFaith robustly improves faithful calibration across diverse models and task domains, enabling up to 61% improvement in faithfulness and achieving an 83% win rate over original generations as judged by humans.
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(D)RAGged Into a Conflict: Detecting and Addressing Conflicting Sources in Retrieval-Augmented LLMs
Arie Cattan
Alon Jacovi
Ori Ram
Eran Ofek
2025
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Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing LLMs with relevant and up-to-date information. However, the retrieved sources can often bring conflicting information and it is not clear how models address such discrepancies. In this work, we first point out that knowledge conflicts stem from various reasons and thus require tailored solutions in order to better align model responses to human preferences. To that end, we introduce a novel taxonomy of knowledge conflicts in RAG and define the desired model’s behavior for each category. Additionally, we construct a high-quality benchmark by asking two expert annotators to identify the conflict type within realistic RAG instances, each comprising a query and its associated search results. Finally, we conduct extensive experiments and show that explicitly informing LLMs about the potential conflict category significantly improves the quality and appropriateness of the responses. Yet, there is still a vast room for improvement. Taken together, our work highlights the importance of evaluating RAG systems not only on factual accuracy but also on their ability to manage and resolve knowledge conflicts effectively.
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Inside-Out: Hidden Factual Knowledge in LLMs
Eyal Ben David
Eran Ofek
Hadas Orgad
Zorik Gekhman
Roi Reichart
Yonatan Belinkov
2025
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This work presents a framework for assessing whether large language models (LLMs) encode more factual knowledge in their parameters than what they express
in their outputs. While a few studies hint at this possibility, none has clearly defined or demonstrated this phenomenon. We first propose a formal definition of knowledge, quantifying it for a given question as the fraction of correct-incorrect answer pairs where the correct one is ranked higher. This gives rise to external and internal knowledge, depending on the information used to score individual answer candidates: either the model’s observable token-level probabilities or its intermediate computations. Hidden knowledge arises when internal knowledge exceeds external knowledge. We then present a case study, applying this framework to three popular open-weights LLMs in a closed-book QA setup. Our results indicate that: (1) LLMs consistently encode more factual knowledge internally than what they express externally, with an average gap of 40%. (2) Surprisingly, some knowledge is so deeply hidden that a model can internally know an answer perfectly, yet fail to generate it even once, despite large-scale repeated sampling of 1,000 answers. This reveals fundamental limitations in the generation capabilities of LLMs, which (3) puts a practical constraint on scaling test-time compute via repeated answer sampling in closed-book QA: significant performance improvements remain inaccessible because some answers are practically never sampled, yet if they were, we would be guaranteed to rank them first.
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As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. In this work, we investigate how multilinguality during instruction tuning of a multilingual LLM affects instruction-following across languages from the pre-training corpus. We first show that many languages transfer some instruction-following capabilities to other languages from even monolingual tuning. Furthermore, we find that only 40 multilingual examples integrated in an English tuning set substantially improve multilingual instruction-following, both in seen and unseen languages during tuning. In general, we observe that models tuned on multilingual mixtures exhibit comparable or superior performance in multiple languages compared to monolingually tuned models, despite training on 10x fewer examples in those languages. Finally, we find that diversifying the instruction tuning set with even just 2-4 languages significantly improves cross-lingual generalization. Our results suggest that building massively multilingual instruction-tuned models can be done with only a very small set of multilingual instruction-responses.
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Systematization, Analysis, and Mitigation of LLMs Hallucinations
Fazl Barez
Zorik Gekhman
Gabriel Stanovsky
Itay Itzhak
Roi Reichart
Yonatan Belinkov
Dana Arad
Adi Simhi
Arxiv (2024)
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Hallucinations in large language models represent a critical barrier to reliable usage. However, existing research tends to focus on categorizing error types by their manifestations rather than by their underlying knowledge-related causes. We propose a novel framework for categorizing hallucinations along two critical dimensions for effective mitigation: knowledge and certainty. Along the knowledge axis, we distinguish between hallucinations caused by a lack of knowledge (HK− ) and those occurring despite the model having the correct knowledge (HK+). Through model-specific dataset construction and comprehensive experiments across multiple models and datasets we show that we can distinguish HK+ and HK− hallucinations. Furthermore, HK+ and HK−
hallucinations exhibit different characteristics, and respond differently to mitigation strategies, with activation steering proving effective only for HK+ hallucinations. We then turn to the certainty axis, identifying a particularly concerning subset of HK+ hallucinations that occur with high certainty, which we refer to as Certainty Misalignment (CC), where models hallucinate with certainty despite having the correct knowledge. To address this, we introduce a new evaluation metric (CC-Score). This reveals significant blind spots in existing mitigation methods, which may perform well on average but fail disproportionately on these critical cases. Our targeted probe-based mitigation approach, specifically designed for CC instances, demonstrates superior performance compared to existing methods (such as internal probing-based and prompting-based). These findings highlight the importance of considering both knowledge and certainty in hallucination analysis and call for more targeted approaches to detection and mitigation that consider their underlying causes.
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Text to image generation methods (T2I) are widely popular in generating art and other creative artifacts.
While hallucination can be a positive factor in scenarios where creativity is appreciated, such artifacts are poorly suited for tasks where the generated image needs to be grounded in a strict manner, e.g. as an illustration of a task, an action or in the context of a story.
In this paper, we propose to strengthen the factual consistency properties of T2I methods in the presence of natural prompts.
First, we cast the problem as an MT problem that translates natural prompts into visual prompts. Then we filter the image with a VQA approach where we answer a set of questions in the visual domain (the image) and in the natural language domain (the natural prompt).
Finally, to measure the alignment of answers, we depart from the recent literature that do string matching, and compare answers in an embedding space that assesses the semantic and entailment associations between a natural prompt and its generated image.
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Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
Brian Gordon
Dani Lischinski
Daniel Cohen-Or
Xi Chen
arXiv (2023)
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While existing image/text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment.
In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text/image pairs.
We leverage large language models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also introduce a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks.
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MaXM: Towards Multilingual Visual Question Answering
Linting Xue
Michal Yarom
Xi Chen
Findings of ACL: EMNLP (2023)
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Visual Question Answering (VQA) has been primarily studied through the lens of the English language. Yet, tackling VQA in other languages in the same manner would require a considerable amount of resources. In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers. Then, we apply our framework to the multilingual captions in the Crossmodal-3600 dataset and develop an efficient annotation protocol to create MaXM, a test-only VQA benchmark in 7 diverse languages. Finally, we develop a simple, lightweight, and effective approach as well as benchmark state-of-the-art English and multilingual VQA models. We hope that our benchmark encourages further research on mVQA.
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Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are robust to shifts in setting (sometimes to more realistic ones), training data size (e.g. from large to small sets) and domain. In this work, we design and conduct the first large-scale robustness study of history modeling approaches for CQA. We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings. Equipped with the insights from our study, we design a novel prompt-based history modeling approach, and demonstrate its strong robustness across various settings. Our approach is inspired by existing methods that highlight historic answers in the passage. However, instead of highlighting by modifying the passage token embeddings, we add textual prompts directly in the passage text. Our approach is simple, easy-to-plug into practically any model, and highly effective, thus we recommend it as a starting point for future model developers. We also hope that our study and insights will raise awareness to the importance of robustness-focused evaluation, in addition to obtaining high leaderboard scores, leading to better CQA systems.
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