Roee Aharoni
Roee Aharoni is a Research Scientist at Google Tel Aviv, working on natural language processing.
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Recent work suggested utilizing inference compute, showing that scaling of number of samples consistently improves the fractions of problems solved by any attempt, namely the coverage. In this work, we suggest that inference scaling gains should be compared with proper baselines, as some datasets become degenerate when allowing a large number of attempts. We focus on two domains - mathematical reasoning and factual knowledge, showing that for the MATH and Entity Questions datasets, informed answer enumeration obtains similar or even better results than repeated model sampling, with a much lower sample budget. While we believe that inference scaling is a promising approach for unlocking the potential of language models, we recommend carefully selecting models and datasets when applying this method. Otherwise, the results of inference scaling should be interpreted with caution.
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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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A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi
Or Honovich
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024), pp. 4615–4634
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Prompting language models to provide step-by-step answers (e.g., “Chain-of-Thought”) is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction. We introduce REVEAL: Reasoning Verification Evaluation, a dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question-answering settings. REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model’s answer, across a variety of datasets and state-of-the-art language models. Evaluation on REVEAL shows that verifiers struggle at verifying reasoning chains — in particular, verifying logical correctness and detecting contradictions. Available at https://reveal-dataset.github.io/.
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We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. For example, if the LLM is equally likely to output two contradicting answers to the same question, then its generated response should reflect this uncertainty by hedging its answer (e.g., "I'm not sure, but I think..."). We formalize faithful response uncertainty based on the gap between the model's intrinsic confidence in the assertions it makes and the decisiveness by which they are conveyed. This example-level metric reliably indicates whether the model reflects its uncertainty, as it penalizes both excessive and insufficient hedging. We evaluate a variety of aligned LLMs at faithfully communicating uncertainty on several knowledge-intensive question answering tasks. Our results provide strong evidence that modern LLMs are poor at faithfully conveying their uncertainty, and that better alignment is necessary to improve their trustworthiness.
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q2d: Automatic Dialog Generation to Improve Models' Query Generation
Enav Weinreb
Ido Hakimi
Shlomi Cohen-Ganor
Yoad Lewenberg
EMNLP 2023 (2023)
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We propose q2d: an automatic data generation pipeline that generates information-seeking dialogues based on questions. We apply our method to create conversational versions of questions answering datasets, which we release as a new dataset. We use this data to improve query generation models, which communicate with an external search APIs to generate factual responses. Unlike previous approaches, which relied on human annotators, our method allows to automatically generate labeled dialogues with better control and scale.
In experiments, we demonstrate that: (1) Models trained on our synthetic data produce results comparable to those trained on natural data; (2) Our generated datasets are effective as a benchmark and as a training signal that generalizes to human-annotated test sets.
We also provide an extensive analysis of the quality and factuality of the generated datasets. Our studies indicate that our automatic dialogue generation pipeline is effective at improving query generation and factuality.
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One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is time and resource consuming.
In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions. We prompt a large language model (PaLM) to create conversational versions of question answering datasets, and use it to improve query generation models that communicate with external search APIs to ground dialog responses. Unlike previous approaches which relied on human written dialogs with search queries, our method allows to automatically generate query-based grounded dialogs with better control and scale.
Our experiments demonstrate that: (1) For query generation on the QReCC dataset, models trained on our synthetically-generated data achieve 90%--97% of the performance of models trained on the human-generated data; (2) We can successfully generate data for training dialog models in new domains without any existing dialog data as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We perform a thorough analysis of the generated dialogs showing that humans find them of high quality and struggle to distinguish them from human-written dialogs.
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SEAHORSE: A Dataset of Summaries Annotated with Human Ratings in Six Languages
Elizabeth Clark
Shruti Rijhwani
Sebastian Gehrmann
EMNLP 2023, Association for Computational Linguistics (2023)
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We introduce Seahorse (SummariEs Annotated with Human Ratings in Six languagEs), a dataset of 96K summaries with ratings along 6 dimensions (comprehensibility, repetition, grammar, attribution, main idea(s), and conciseness). The summaries are generated from 8 different models, conditioned on source text from 4 datasets in 6 languages (German, English, Spanish, Russian, Turkish, and Vietnamese). We release the annotated summaries as a resource for developing better summarization models and automatic metrics. We present an analysis of the dataset's composition and quality, and we demonstrate the potential of this dataset for building better summarization metrics, showing that metrics finetuned with Seahorse data outperform baseline metrics.
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A Comprehensive Evaluation of Tool-Assisted Generation Strategies
Alon Jacovi
Findings of EMNLP (2023)
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A growing area of research investigates augmenting language models with tools (e.g., search engines, calculators) to overcome their shortcomings (e.g., missing or incorrect knowledge, incorrect logical inferences). Various few-shot tool-usage strategies have been proposed. However, there is no systematic and fair comparison across different strategies, or between these strategies and strong baselines that do not leverage tools. We conduct an extensive empirical analysis, finding that (1) across various datasets, example difficulty levels, and models, strong no-tool baselines are competitive to tool-assisted strategies, implying that effectively using tools with in-context demonstrations is a difficult unsolved problem; (2) for knowledge-retrieval tasks, strategies that *refine* incorrect outputs with tools outperform strategies that retrieve relevant information *ahead of* or *during generation*; (3) tool-assisted strategies are expensive in the number of tokens they require to work -- incurring additional costs by orders of magnitude -- which does not translate into significant improvement in performance. Overall, our findings suggest that few-shot tool integration is still an open challenge, emphasizing the need for comprehensive evaluations of future strategies to accurately assess their *benefits* and *costs*.
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Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often differ in their characteristics from real model-generated summaries and have limited coverage of possible factual errors. Alternatively, large language models (LLMs) have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diverse model-generated summaries using a LLM. Unlike prior work, TrueTeacher does not rely on human-written summaries, and is multilingual by nature. Experiments on the TRUE benchmark show that a student model trained using our data, substantially outperforms both the state-of-the-art model with similar capacity, and the LLM teacher. In a systematic study, we compare TrueTeacher to existing synthetic data generation methods and demonstrate its superiority and robustness to domain-shift. We also show that our method generalizes to multilingual scenarios using the mFACE dataset. Finally, we release a large-scale synthetic dataset with 1.4M examples generated using TrueTeacher.
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What You See is What You Read? Improving Text-Image Alignment Evaluation
Michal Yarom
Eran Ofek
arXiv (2023)
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Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic image-text alignment evaluation. We first introduce a comprehensive evaluation set spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach based on synthetic data generation. Both methods surpass prior approaches in various text-image alignment tasks, with our analysis showing significant improvements in challenging cases that involve complex composition or unnatural images. Finally, we demonstrate how our approaches can localize specific misalignments between an image and a given text, and how they can be used to automatically re-rank candidates in text-to-image generation.
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