United States

Evaluating reasoning quality in multi-agent LLM systems is challenging, especially for open-ended tasks without reference answers. We investigate whether intrinsic confidence signals, token-level log-probabilities from decoding, can predict reasoning quality as assessed by LLM-as-judge evaluation. Using a debate-based essay scoring framework, we compare confidence proxies against rubric-based judge scores across two ASAP essay sets. We find that early-token confidence, particularly within the first few generated tokens, is consistently the strongest predictor of reasoning quality, outperforming full-sequence statistics. Analysis of log-probability trajectories shows that the opening phase of generation is the most heterogeneous and therefore most informative. We also observe a systematic asymmetry between agent roles, with stronger alignment between confidence and quality for supportive reasoning than for adversarial critique. These results suggest that early decoding dynamics provide a lightweight and effective signal for estimating reasoning reliability in multi-agent LLM systems.

ACL 2026

Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate

llm-as-judge

reasoning evaluation

confidence estimation

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

Large language models (LLMs) are increasingly used as judges of chain-of-thought (CoT) reasoning, yet it remains unclear whether they can reliably assess process faithfulness rather than merely answer plausibility. We introduce C2-Faith, a benchmark built from PRM800K that explicitly decomposes faithfulness into two complementary dimensions: causality (whether each step logically follows from prior context) and coverage (whether essential intermediate inferences are present). Using controlled perturbations, we construct examples with known causal error positions by replacing a single step with a logically inconsistent variant, and with controlled coverage deletions at varying rates, enabling direct measurement against reference labels. We evaluate three frontier LLM judges across three tasks: binary causal detection, causal step localization, and coverage scoring. Our results reveal that judge reliability is highly task-dependent, with no single model dominating across settings. While models often detect that an error exists, they struggle to accurately localize it, indicating a substantial gap between detection and attribution. Moreover, all judges systematically overestimate reasoning completeness, assigning high coverage scores even when substantial portions of intermediate reasoning are missing. These findings expose fundamental limitations of LLM judges in process-level evaluation and highlight the need for more reliable and calibrated methods when using LLMs to assess reasoning quality.

C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning

We present a multilingual study of sentiment evaluation on Wikipedia articles from various topics in five languages (German, English, Spanish, Polish, and Russian). In this paper, we compare three large language models (Gemini Pro 3.1, Claude Opus 4.6, and GPT 5.2), each queried three times per sentence, with two popular multilingual sentiment classifiers. This setup allows us to analyze not only inter-model differences but also intra-model stability as a proxy for confidence. To support systematic evaluation, we construct a benchmark dataset based on strict consensus across evaluators and analyze sentiment distributions across topics and languages. We show substantial variation in sentiment distributions, agreement, and consistency across models and languages. Our results suggest that sentiment evaluation on encyclopedic text remains an underexplored challenge for multilingual NLP.

Evaluating Multilingual Sentiment Classifiers Using an LLM-Annotated Wikipedia Benchmark

Recent advances in language modeling have led to a growing number of papers related to finance in top-tier Natural Language Processing (NLP) venues. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 quantitative and qualitative dimensions, with particular attention to evaluation practices, metric choices, dataset coverage, and reproducibility in a high-stakes applied LM domain. Our study identifies the following opportunities for NLP researchers: (i) expanding the scope of forecasting tasks; (ii) enriching evaluation with finance-specific metrics; (iii) leveraging multilingual and crisis-period datasets for robustness-oriented evaluation; and (iv) balancing PLMs with efficient or interpretable alternatives. We identify actionable directions supported by dataset and tool recommendations, with implications for both academic evaluation practices and industry deployment.

Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities

We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The benchmark comprises 360 curated tasks from 82 authoritative websites spanning domains such as macroeconomics, labor, finance, trade, and public policy. Each task challenges agents to navigate live websites, interpret structured and visual content, interact with real interfaces, and extract precise, time-sensitive data through multi-step workflows. We construct the benchmark by prompting multiple large language models (LLMs) to generate candidate tasks, followed by rigorous human curation to ensure clarity, feasibility, and source reliability. Unlike prior work, EconWebArena emphasizes fidelity to authoritative data sources and the need for grounded web-based economic reasoning. We evaluate a diverse set of state-of-the-art multimodal LLMs as web agents, analyze failure cases, and conduct ablation studies to assess the impact of visual grounding, plan-based reasoning, and interaction design. Our results reveal substantial performance gaps and highlight persistent challenges in grounding, navigation, and multimodal understanding, positioning EconWebArena as a rigorous testbed for economic web intelligence.

EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments

LLMs have demonstrated exceptional proficiency in a wide range of NLP tasks. However, a notable gap remains in practical data analysis scenarios, particularly when LLMs are required to process long sequences of unstructured documents, such as news feeds or, as specifically addressed in this paper, social media posts. To empirically assess the effectiveness of LLMs in this setting, we introduce a question-based evaluation framework comprising 470 manually curated questions designed to evaluate LLMs' semantic understanding and reasoning abilities over aggregated text data. We apply our benchmark on diverse Twitter datasets covering various NLP tasks, including sentiment analysis, hate speech detection, and emotion recognition. Our results reveal that the performance depends heavily on input scale and the complexity of the data sources, declining noticeably in multi-label or target-dependent scenarios. In addition, as task complexity increases, performance drops progressively from basic semantic existence identification to more demanding operations such as comparison, counting, and calculation. Furthermore, as the input size grows beyond 500 instances, we identify a common limitation across LLMs, particularly Open-weights models: performance degrades substantially, especially on numerical tasks. These findings highlight critical architectural bottlenecks in current LLMs for performing rigorous quantitative analysis over large text collections.

Text Analytics Evaluation Framework: A Case Study on LLMs and Social Media

Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge graphs from scientific corpora using ontology-guided LLM extraction to enable structured, large-scale trend analysis. Applied to 681 papers on GenAI in Finance (2022--2025), MetaGraph reveals three phases: early LLM-driven expansion of tasks and datasets, growing emphasis on limitations and risk, and a shift toward modular, system-oriented methods (e.g., retrieval-augmented designs). We release the resulting resource and artifacts to support reproducible meta-analysis and future monitoring of the field.

MetaGraph: A Large-Scale Meta-Analysis of GenAI in Finance NLP (2022–2025)

Evaluating conversational AI systems that use external tools is challenging, as errors can arise from complex interactions among user, agent, and tools. While existing evaluation methods assess either user satisfaction or agents' tool-calling capabilities, they fail to capture critical errors in multi-turn tool-augmented dialogues—such as when agents misinterpret tool results yet appear satisfactory to users. We introduce TRACE, a benchmark of systematically synthesized tool-augmented conversations covering diverse error cases. Evaluation with state-of-the-art conversation evaluation frameworks reveals that all approaches remain far from ideal performance, demonstrating the fundamental difficulty of this benchmark.

Multi-Faceted Evaluation of Tool-Augmented Dialogue Systems

We present a domain-grounded benchmark and evaluation framework for tool-aware plan generation in contact-center analytics, where answering a business-insights query requires decomposing it into executable steps over structured tools (Text2SQL over Snowflake), unstructured tools (RAG over transcripts), and LLM-based synthesis, with explicit depends_on relations for safe parallel execution. Our contributions are threefold: (i) a reference-based plan evaluation framework with two complementary views—a metric-wise evaluator spanning seven dimensions (e.g., tool–prompt alignment, query adherence) and a one-shot evaluator that compares a candidate plan against a reference plan; (ii) a lineage-driven data curation methodology that uses an iterative evaluator→optimizer loop to refine initial plans into high-quality plan lineages while reducing manual effort; and (iii) a large-scale study of 14 LLMs across model families and sizes on their ability to generate step-by-step, executable, tool-assigned plans, evaluated with and without lineage in the prompt. Empirically, LLMs continue to struggle on compound queries and on plans longer than four steps; the highest aggregate metric-wise score is 84.8 (Claude-3-7-Sonnet), while the strongest one-shot A+ rate (Extremely Good or Very Good) is only 49.75% (o3-mini). Lineage yields mixed overall gains but improves several strong models and often helps step executability. Overall, our results expose persistent weaknesses in tool understanding—especially tool–prompt alignment and tool-usage completeness—and show that shorter, simpler plans remain markedly easier. The benchmark, evaluation framework, and findings provide a practical path for assessing and improving agentic planning with tools in enterprise question-answering settings. An anonymized dataset with human-annotated reference plans, plan lineages, and per-planner outputs for all 14 planners is available at the anonymous repository linked in the paper.

Tool-Aware Planning in Contact Center AI: Evaluating LLMs through Lineage-Guided Query Decomposition

Time series data are integral to applications across domains such as finance, healthcare, transportation, and environmental science. While recent work has begun to explore time series question answering (QA), existing benchmarks still provide limited coverage of analytical capabilities under a standardized evaluation framework. We introduce TSAQA, a novel unified benchmark designed to broaden task coverage and evaluate diverse temporal analysis capabilities. TSAQA integrates 6 diverse tasks under a single framework ranging from conventional analysis, including anomaly detection and classification, to advanced analysis, such as characterization, comparison, data transformation, and temporal relationship analysis. Spanning 210k samples across 13 domains, the dataset employs diverse formats, including true-or-false (TF), multiple-choice (MC), and a novel puzzling (PZ), to comprehensively assess time series analysis. Zero-shot evaluation shows that TSAQA remains challenging for current Large Language Models (LLMs): best-performing commercial model, Gemini-2.5-Flash, achieves 65.08 average accuracy. Although instruction tuning improves open-source models’ performance: the best- performing model, LLaMA-3.1-8B, shows significant room for improvement. We further evaluate language-capable time series foundation models (TSFMs), showing that TSAQA extends beyond general-purpose LLMs. The data are available in https://huggingface.co/datasets/TSAQA/TSAQA-Benchmark.

TSAQA: Time Series Analysis Question And Answering Benchmark

Prior research demonstrates that the performance of language models on reasoning tasks can be influenced by suggestions, hints, and endorsements. However, the influence of endorsement source credibility remains underexplored. We investigate whether language models exhibit systematic bias based on the perceived expertise of the provider of the endorsement. Across 4 datasets spanning mathematical, legal, and medical reasoning, we evaluate 11 models using personas representing four expertise levels per domain. Our results reveal that models are increasingly susceptible to incorrect or misleading endorsements as source expertise increases, with higher-authority sources inducing not only accuracy degradation but also increased confidence in wrong answers. We also show that this authority bias is mechanistically encoded within the model and a model can be steered away from the bias, thereby improving its performance even when an expert gives a misleading endorsement.

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C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning

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Thesis Proposal: When Does Confidence Signal Quality? Log-Probabilities and LLM-as-Judge in Multi-Agent Debate

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Next from ACL 2026

C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning

Similar lecture

Thesis Proposal: When Does Confidence Signal Quality? Log-Probabilities and LLM-as-Judge in Multi-Agent Debate

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