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Speculative decoding accelerates large language model inference using a smaller draft model. In this paper, we establish a surprising connection between speculative decoding and channel simulation, which aims at simulating a noisy channel using as few bits as possible. This connection allows us to provide an information-theoretic analysis of the speed up that can be achieved by speculative decoding. Leveraging this link, we derive an explicit relation between generation speed-up and the number of tokens k generated by the draft model for large k, which serves as an upper bound for all k. We also propose a novel speculative decoding method via exponential races ERSD that matches state-of-the-art performance.

ACL 2025

Speculative Decoding via Exponential Races

speculative decoding; information theory; compression

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Current evaluation practices in Simultaneous Speech Translation (SimulST) systems typically involve segmenting the input audio and corresponding translations, calculating quality and latency metrics for each segment, and averaging the results. Although this approach may provide a reliable estimation of translation quality, it can lead to misleading values of latency metrics due to an inherent assumption that average latency values are good enough estimators of SimulST systems' response time. However, our detailed analysis of latency evaluations for state-of-the-art SimulST systems demonstrates that latency distributions are often skewed and subject to extreme variations. As a result, the mean in latency metrics fails to capture these anomalies, potentially masking the lack of robustness in some systems and metrics. In this paper, a thorough analysis of the results of systems submitted to recent editions of the IWSLT simultaneous track is provided to support our hypothesis and alternative ways to report latency metrics are proposed in order to provide a better understanding of SimulST systems' latency.

Going Beyond Your Expectations in Latency Metrics for Simultaneous Speech Translation

Auto-regressive decoding is a memory-bound job, meaning decoding inference performance is limited by the bandwidth rather than the computational capabilities of the GPU. Weight-only quantization is a promising method to address the memory-bound limitations. Previous studies have followed one of two approaches. Some have exclusively studied integer quantization while ignoring the Gaussian distribution nature of LLMs' weights. Others have proposed non-uniform quantization but incurred additional I/O overhead due to lookup tables, e.g. NF4. In this work, we extend the IEEE 754 float-point standard to the ExMy quantization schema, which allocates x bit for the exponent and y bit for the mantissa to represent a number. In terms of runtime efficiency, we demonstrate that the conversion from ExMy to FP16 can be realized through register-level operations, which can get almost the same performance as INT5. In terms of quantization loss, we analyze that of different ExMy settings, where the E2M2 schema achieves an optimal balance, offering the highest efficiency with lossless accuracy. We further propose the FPE2M2 framework that supports lossless weight-only quantization inference and validate the FPE2M2 framework on Qwen and LLaMA Models across various modalities, such as text, image, and audio tasks, which achieves a faster inference speed while maintaining nearly lossless accuracy.

FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point

Recent smaller language models such Phi-3.5 and Phi-4 rely on synthetic data generated using larger language models. Questions remain about leveraging synthetic data for other use cases, such as adapting LLMs to specific domains. A key limitation of synthetic data is low diversity, which negatively impacts its downstream applicability for improving other models. To address this, we propose MetaSynth, a method for generating synthetic data that enhances diversity through meta-prompting, where a language model orchestrates multiple expert LLM agents to collaboratively generate data. Using only 25 million tokens of synthetic data generated with MetaSynth, we successfully adapt a well-trained LLM (Mistral-7B) to two specialized domains--Finance and Biomedicine--without compromising the capabilities of the resulting model in general tasks. In addition, we evaluate the diversity of our synthetic data using seven automated metrics, and find that it approaches the diversity of LLM pre-training corpora.

MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation

Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed for real-world applications such as multimodal code generation, fake news detection, or scene understanding, it has not been thoroughly studied in the literature. In this paper, we introduce a new task and benchmark to address this gap. Our benchmark, MATE, consists of 5.5k evaluation instances featuring visual scenes aligned with their textual representations. To evaluate cross-modal entity linking performance, we design a question-answering task that involves retrieving one attribute of an object in one modality based on a unique attribute of that object in another modality. We evaluate state-of-the-art Vision-Language Models (VLMs) and humans on this task, and find that VLMs struggle significantly compared to humans, particularly as the number of objects in the scene increases. Our analysis also shows that, while chain-of-thought prompting can improve VLM performance, models remain far from achieving human-level proficiency. These findings highlight the need for further research in cross-modal entity linking and show that MATE is a strong benchmark to support that progress.

Vision-Language Models Struggle to Align Entities across Modalities

Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires significant perceptual and cognitive effort. Chart Question Answering (CQA) systems automate this process by enabling models to interpret and reason with visual representations of data. However, existing benchmarks like ChartQA lack real-world diversity and have recently shown performance saturation with modern large vision-language models (LVLMs). To address these limitations, we introduce ChartQAPro, a new benchmark that includes 1,341 charts from 157 diverse sources, spanning various chart types—including infographics and dashboards—and featuring 1,948 questions in various types, such as multiple-choice, conversational, hypothetical, and unanswerable questions, to better reflect real-world challenges. Our evaluations with 21 models show a substantial performance drop for LVLMs on ChartQAPro; e.g., Claude Sonnet 3.5 scores 90.5% on ChartQA but only 55.81% on ChartQAPro, underscoring the complexity of chart reasoning. We complement our findings with detailed error analyses and ablation studies, identifying key challenges and opportunities for advancing LVLMs in chart understanding and reasoning. We release ChartQAPro at <redacted>.

ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering

Human activity is moderated by norms. However, machines are often trained without explicit supervision on norm understanding and reasoning, especially when the norms are grounded in a physical and social context. To improve and evaluate the normative reasoning capability of vision-language models (VLMs), we present EgoNormia, consisting of 1,853 ego-centric videos of human interactions, each of which has two related questions evaluating both the prediction and justification of normative actions. The normative actions encompass seven categories: safety, privacy, proxemics, politeness, cooperation, coordination/proactivity, and communication/legibility. To compile this dataset at scale, we propose a novel pipeline leveraging video sampling, automatic answer generation, filtering, and human validation. Our work demonstrates that current state-of-the-art vision-language models lack robust norm understanding, scoring a maximum of 54% on EgoNormia (versus a human bench of 92%). Our analysis of performance in each dimension highlights the significant risks of safety, privacy, and the lack of collaboration and communication capability when applied to real-world agents. We additionally show that through a retrieval-based generation method, it is possible to use EgoNormia to enhance normative reasoning in VLMs.

EgoNormia: Benchmarking Physical Social Norm Understanding

This study proposes a simple yet effective LoRA merge method to achieve LLM adaptation for low-resource language generation tasks. The LoRA merge technique, which integrates multiple LoRA modules trained on different tasks, has gained attention as an effective and efficient approach for adapting LLMs to target tasks. However, previous methods are limited in adaptability as they keep the LoRA parameters frozen. Additionally, the low-resource problem has been out of their scope. We propose a LoRA merge method that updates LoRA parameters during the merge process to improve task-specific adaptability. Furthermore, we further enhance the adaptability by parameter pruning that allows finer-grained adjustments of LoRA parameters during merging. Extensive experiments have been conducted taking summarization as a benchmark task. Our datasets cover various domains and multiple languages of English and Japanese. The results confirm that the proposed method achieves significant and consistent improvements in task adaptability over the previous methods.

Adaptive LoRA Merge with Parameter Pruning for Low-Resource Generation

Speculative decoding (SD) accelerates Large Language Model (LLM) generation by using an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. We focus on retrieval-based SD where the draft model retrieves the next tokens from a non-parametric datastore. Sparse retrieval \cite[REST], which operates on the surface form of strings, is currently the dominant paradigm due to its simplicity and scalability. However, its effectiveness is limited due to the usage of short contexts and exact string matching. Instead, we introduce Dense Retrieval for Speculative Decoding (DReSD), a novel framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant token sequences for SD. Extensive experiments show that DReSD achieves (on average) 87% higher acceptance rates, 65% longer accepted tokens and 19% faster generation speeds compared to sparse retrieval (REST).

DReSD: Dense Retrieval for Speculative Decoding

Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally expensive, as sampling many of these (lengthy) paths is required to increase the chances that the correct answer emerges as the most frequent one. To address this, we introduce Confidence-Informed Self-Consistency (CISC). CISC performs a weighted majority vote based on confidence scores obtained directly from the model. By prioritizing high-confidence paths, it can identify the correct answer with a significantly smaller sample size. When tested on nine models and four datasets, CISC outperforms self-consistency in nearly all configurations, reducing the required number of reasoning paths by over 40% on average. In addition, we introduce the notion of within-question confidence evaluation, after showing that standard evaluation methods are poor predictors of success in distinguishing correct and incorrect answers to the same question. In fact, the most calibrated confidence method proved to be the least effective for CISC. Lastly, beyond these practical implications, our results and analyses show that LLMs can effectively judge the correctness of their own outputs, contributing to the ongoing debate on this topic.

Confidence Improves Self-Consistency in LLMs

Multimodal large language models (MLLMs) have shown remarkable capabilities across various downstream tasks. However, when MLLMs are transferred to the text image machine translation (TiMT) task, preliminary experiments reveal that MLLMs suffer from serious repetition and omission hallucinations. To alleviate these issues, this paper first designs an efficient MLLM named InImageTrans for TiMT and then proposes a simple and effective method named multi-conditional direct preference optimization (mcDPO) for advancing the TiMT. Particularly, the proposed mcDPO not only guides the MLLM in rejecting repetition output by creating text output preference pairs automatically, but also guides the MLLM in paying more attention to text information in images by creating image input preference pairs. Furthermore, we build a high-quality benchmark called MCiT for comprehensively evaluating the TiMT capabilities of InImageTrans. Experimental results show that the proposed method significantly outperforms existing open-source MLLMs on MCiT. All data and codes will be released once accepted.

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Going Beyond Your Expectations in Latency Metrics for Simultaneous Speech Translation

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