United States

We design experiments to identify where Large Language Models (LLMs) struggle when answering complex questions. Our focus is on two key stages, mirroring the human QA process: 1) \textit{question decomposition}, where the model breaks down a complex question into sub-questions and 2) \textit{subproblem solving}, where it addresses each sub-question to obtain the final response. We preprocess and expand three multi-hop datasets to create experimental datasets featuring explicit and implicit multi-hop questions, crowdsourced and templated questions, and varying numbers of hops. Our results show that larger models (Llama 3.1 70B and o1) excel at decomposing explicit multi-hop questions but struggle with implicit ones, while smaller models (e.g., Llama 3.1 8B) have difficulty with both. In the sub-problem solving stage, all models perform well on simple questions with context. Furthermore, we found no correlation between accuracy in the question decomposition stage and final QA performance (direct response), highlighting a key difference between human and LLM reasoning.

ACL 2026

Identifying Where Large Language Models Struggle in Answering Complex Questions

multihop qa

complex questions

reasoning

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently *post-hoc*. Disconnected from the active execution loop, such assessments identify errors that are usually addressed through prompt-tuning or retraining, and fundamentally cannot course-correct the agent in real time. To close this gap, we move evaluation into the execution loop at *inference time*: a specialized reviewer agent evaluates provisional tool calls *prior to* execution, shifting the paradigm from post-hoc recovery to proactive evaluation and error mitigation. In practice, this architecture establishes a clear separation of concerns between the primary execution agent and a secondary review agent. As with any multi-agent system, the reviewer can introduce new errors while correcting others, yet no prior work to our knowledge has systematically measured this tradeoff. To quantify this tradeoff, we introduce *Helpfulness-Harmfulness metrics*: helpfulness measures the percentage of base agent errors that feedback corrects; harmfulness measures the percentage of correct responses that feedback degrades. These metrics directly inform reviewer design by revealing whether a given model or prompt provides net positive value. We evaluate our approach on BFCL (single-turn) and $\tau^2$-Bench (multi-turn stateful scenarios), achieving +5.5\% on irrelevance detection and +7.1\% on multi-turn tasks. Our metrics reveal that reviewer model choice is critical: the reasoning model o3-mini achieves a 3:1 benefit-to-risk ratio versus 2.1:1 for GPT-4o. Automated prompt optimization via GEPA provides an additional +1.5--2.8\%. Together, these results demonstrate a core advantage of separating execution and review: the reviewer can be systematically improved through model selection and prompt optimization, without retraining the base agent.

Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents

Automated grading of open-ended student answers is a natural setting for **LLM-as-evaluator** research, yet existing methods break down when students take diverse solution approaches to the same problem. We present **DynoGrader**, a human-in-the-loop (HITL) grading system that grounds every LLM judgment in explicit evidence quotes and applies approach-specific rubrics, along with **DynoBench**, a benchmark of 12,820 human-graded responses spanning two university courses. Spending under 9 minutes of TA time per question on lightweight HITL approach discovery (avg. 12 out of 120 unreviewed submissions), DynoGrader reduces MAE by 5.2% over the strongest LLM-evaluator baseline ($p < 0.001$)—recovering 90% of the gain from fully expert-annotated approaches, with no expert pre-annotation required. *This work is under review at ACL 2026.*

Reviewing Only 1/10 of Submissions: Efficient Human-in-the-Loop Discovery for Approach-Aware LLM Grading

When recalling information in conversation, people often arrive at the recollection after multiple turns. However, existing benchmarks for evaluating agent capabilities in such tip-of-the-tongue search processes are restricted to single-turn settings. To more realistically simulate tip-of-the-tongue search, we introduce Dual-agent based Evaluation Through Obscure Under-specified Retrieval (DETOUR), a dual-agent evaluation benchmark containing 1,011 prompts. The benchmark design involves a Primary Agent, which is the subject of evaluation, tasked with identifying the recollected entity through querying a Memory Agent that is held consistent across evaluations. Our results indicate that current state-of-the-art models still struggle with our benchmark, only achieving 36% accuracy when evaluated on all modalities (text, image, audio, and video), highlighting the importance of enhancing capabilities in underspecified scenarios.

DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning

With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--``write like me''--has become a rapidly growing area of interest. However, style personalization is highly specific, relative to every user, and depends strongly on the pragmatic context, which makes it uniquely challenging. Although prior research has introduced benchmarks and metrics for this area, they tend to be non-standardized and have known limitations (e.g., poor correlation with human subjects). LLMs have been found to not capture author-specific style well, it follows that the metrics themselves must be scrutinized carefully. In this work we critically examine the effectiveness of the most common metrics used in the field, such as BLEU, embeddings, and LLMs-as-judges. We evaluate these metrics using our proposed style discrimination benchmark, which spans eight diverse writing tasks across three evaluation settings: $\textit{domain discrimination}$, $\textit{authorship attribution}$, and $\textit{LLM-generated personalized vs non-personalized discrimination}$. We find strong evidence that employing ensembles of diverse evaluation metrics consistently outperforms single-evaluator methods, and provide guidance on how to reliably assess style-personalized text generation.

Evaluating Style-Personalized Text Generation: Challenges and Directions

Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quality issues stemming from annotator misunderstanding or lack of engagement. To address this, we introduce a real-time requirement adherence (RE-AD) framework that leverages LLMs to proactively validate labeling quality. Our methodology involves decomposing Standard Operating Procedures (SOPs) into atomic rules via self-reflection, categorizing them by complexity, and applying tiered validation strategies. Evaluated on a synthetic benchmark, the system achieved an F1 score of 0.749. Furthermore, production deployment resulted in annotators accepting and fixing 82% of the errors flagged by the framework. We include ablation studies to demonstrate the impact of our core design decisions.

RE-AD: Real-Time Requirement Adherence for Data Labeling

General-purpose language models are trained to produce varied natural language outputs, but for some tasks, like annotation or classification, we need more specific output formats. LLM systems increasingly support structured output, which enforces formats by sampling tokens according to a grammar — but also unpredictably reduces downstream performance. Are there systematic differences between grammars that appear semantically (and often visually) similar to humans? To answer this, we test four popular model families with five varying output formats on four common NLP benchmarks. We find all models perform most accurately when guided to use formats respecting convention, such as letters for multiple choice and real numbers for numerical prediction. Performance also improves by 5%-10% when guiding models to return tokens incorporating leading whitespace, with smaller models benefiting the most. We find leading whitespace helps models avoid structural deficiencies in subword token representations. We finally present best practices for researchers using language models as zero-shot classifiers with structured output.

Lost in Space: Finding the Right Tokens for Structured Output

As large language models (LLMs) continue to advance, reliable evaluation methods are essential—particularly for open-ended, instruction-following tasks. LLM-as-a-Judge enables automatic evaluation using LLMs as evaluators, but its reliability remains uncertain. In this work, we analyze key factors affecting its trustworthiness, focusing on alignment with human judgments and evaluation consistency. Using BIGGENBench and EvalBiasBench, we study the effects of evaluation design, decoding strategies, and Chain-of-Thought (CoT) reasoning in evaluation. Our results show that evaluation criteria are critical for reliability, non-deterministic sampling improves alignment with human preferences over deterministic evaluation, and CoT reasoning offers minimal gains when clear evaluation criteria are present.

An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability

In many human-annotated NLP tasks involving ambiguity or subjective judgment, annotator disagreement reflects epistemic uncertainty rather than noise. Soft labeling (SL), which represents annotations as probability distributions rather than majority-vote (MV) labels, preserves this uncertainty and can improve downstream performance. We extend this perspective to LLM-based annotation by formalizing LLM soft labeling as introducing controlled variation in model-generated annotations to approximate the latent variability underlying human annotations. We distinguish two sources of variation: model-induced (e.g., stochastic decoding and model ensembles) and human-approximated (e.g., persona prompting and human-calibrated in-context annotation). Using the Gab Hate and GoEmotions datasets, we show that SL training consistently outperforms MV training under stronger LLM-based annotation strategies. Model ensembles produce the most informative soft-label distributions, achieving the best human–LLM agreement and downstream classification performance. These findings suggest that scalable LLM-based annotation pipelines can model epistemic uncertainty through diverse model-level variation without explicitly simulating human attributes.

Capturing Epistemic Uncertainty in LLM-Based Soft Labeling

Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as low-resource ones (e.g. Swahili, Telugu). In addition they have shown impressive capabilities in different domains, like coding, science and math. In this paper, taking math as an example domain, we study the performance of different LLMs across languages. Experimental results show that there exists a non-negligible and consistent gap in the performance of the models across languages. Interestingly, and somewhat against expectations, the gap exists for both high- and low-resource languages. These results should impact further research into cross-lingual capability generalization for next generation LLMs. Or they would, if it weren't for the fact that they are false. By analyzing one of the standard multilingual math benchmarks (MGSM), we determine that several translation errors are present in the data. Furthermore, the lack of standardized answer extraction from LLM outputs further influences the final results. We propose a method for semi-automatic quality assurance to address the first issue at scale, and give recommendations to address the second one. Combining these two approaches we show that the aforementioned language gap mostly disappears, leading to completely different conclusions from our research. We additionally release the corrected dataset to the community.

Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results

Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assessed only through overall-response judgments. We introduce MCJudgeBench, a benchmark for constraint-level judge evaluation in multi-constraint instruction following. Each instance includes an instruction, a candidate response, an explicit constraint list, per-constraint gold labels in {yes, partial, no}, and controlled response-side perturbations. The evaluation protocol further includes evaluation prompt variants to test judge stability. We evaluate proprietary and open-source LLM judges using both correctness and inconsistency metrics, distinguishing intrinsic inconsistency under stochastic decoding from procedural inconsistency under prompt and response perturbations. Our results show that judge reliability has multiple dimensions: strong overall performance does not guarantee equally reliable detection across label categories, especially for rarer partial and no cases. Judges with higher correctness do not always have lower inconsistency. Evaluation with reasoning improves correctness but does not uniformly improve stability. These findings motivate evaluating LLM judges at the constraint level to study these failure modes.

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