4arXiv cs.CL (Computation and Language)·
DECODEM: Benchmark for LLM extraction of corporate governance variables from legal documents
DECODEM introduces benchmark datasets pairing corporate charters and bylaws with human annotations for evaluating automated extraction of governance variables using LLMs. The paper evaluates multiple extraction pipelines varying in prompt design, task decomposition, and document handling, finding frontier models achieve high accuracy on most provisions. A key finding is that elaborate prompting and cascading pipelines do not consistently improve frontier model performance but substantially narrow the gap with smaller efficiency-oriented models, suggesting pipeline design can partially substitute for model capability in structured legal document extraction.
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CM-LRS: A capital markets reliability benchmark for LLM workflow outputs
Researchers introduce CM-LRS (Capital Markets LLM Reliability Score), a seven-dimension evaluation framework assessing LLM outputs at the workflow level rather than the question-answer layer, targeting regulated capital-markets use cases such as DCM/ECM term extraction, M&A comparables, and issuer profiling. The benchmark is demonstrated on five workflows using public SEC EDGAR and UK takeover filings, scoring four models across four LLM judges. Key findings: frontier closed-source models cluster tightly (Sonnet 4.6 = 4.31, Opus 4.7 = 4.30, GPT-5.5 = 4.09) while Llama 3.3 70B lags at 3.15, with the gap concentrated in retrieval and synthesis tasks rather than extraction. The work advances domain-specific evaluation methodology for high-stakes financial workflows where regulatory defensibility matters.
DataGovBench: Benchmarking LLMs on Real-World Governmental Data Analysis Tasks
Researchers introduce DataGovBench, a benchmark derived from governmental open data designed to evaluate LLMs on practical data analysis tasks beyond simple fact retrieval from small tables. The benchmark covers two tasks: Table QA (complex decomposable questions with textual or visualization outputs) and Table Insight (exploratory data analysis for expert-level findings). Experiments with state-of-the-art LLMs, with and without agentic frameworks, reveal significant performance gaps, suggesting current systems fall well short of real-world data analytics demands.
Paper challenges LLM expert-level claims by measuring variance and error magnitude in code-based data analysis tasks
A new arXiv paper argues that standard LLM benchmarks overstate model capabilities by focusing on average performance on training-data-adjacent tasks while ignoring response variance and error magnitude. The authors introduce a novel benchmark requiring frontier LLMs to write code for data analysis tasks, comparing results against human expert submissions. Human experts outperformed the frontier LLM on average across multiple metrics and showed lower performance variability. The findings challenge the prevailing narrative that LLMs perform at human-expert level on knowledge economy tasks.
German Central Bank case study applies LLMs to securities prospectus eligibility verification
Researchers present the first case study applying LLMs to the Deutsche Bundesbank's securities collateral eligibility verification process, replacing traditional NER-based pipelines with a generative information extraction approach. The system decomposes the task into extraction, normalization, and interpretation stages, handling OCR noise and bilingual (German-English) content in lengthy financial prospectuses. Results show up to 91% precision in document-level eligibility decisions with a conservative false-acceptance profile. The paper also introduces an LLM-as-a-judge evaluation methodology for semantic assessment of extraction quality.
DEFINED: Data-efficient framework for fine-grained creativity assessment in debate using LLMs
DEFINED is a computational framework for automated creativity assessment in debate scenarios, operationalizing creativity through an eight-dimensional hierarchical metric system implemented via a pretrained autoregressive language model with a hierarchical scoring head. The system addresses data scarcity through constrained data augmentation and mixed-granularity training from limited expert-annotated data. It outperforms prompt-based LLM evaluators and existing debate scoring methods on authentic competition data. The work is relevant to AI evaluation methodology and the broader question of whether LLMs can reliably assess complex human cognitive outputs.
Benchmark and study of LLM agents in deliberative multi-agent collaboration under partial observability
Researchers introduce a scalable benchmark for evaluating LLM agents on cooperative joint decision-making tasks where agents must exchange information under partial and asymmetric observations to reach a shared decision. A systematic evaluation of representative LLMs finds that state-of-the-art models still struggle with complex deliberative collaboration, failing in either information alignment or downstream reasoning even with external mathematical tools. Diagnostic analysis also reveals that deliberation can enable reflection and error correction, sometimes outperforming centralized baselines, offering a nuanced picture of multi-agent LLM capabilities.
Dep-LLM: Training-free depression diagnosis framework using structured multi-factor LLM reasoning
Dep-LLM is a training-free framework for automatic depression detection from clinical interviews that uses frozen foundation LLMs without fine-tuning. The system decomposes long clinical dialogues into five thematic factors via Chain-of-Thought analysis, applies token-level entropy-based confidence modulation, and integrates multi-factor signals for final diagnosis. Evaluated on DAIC-WOZ and E-DAIC datasets, it outperforms zero-shot baselines across 21 foundation LLMs and surpasses supervised domain-specific and commercial LLMs on multiple metrics.
RRBench: evaluating open-weight LLMs on longitudinal data preparation tasks in governance-restricted settings
Researchers from UCL introduce RRBench, an open-source benchmark and evaluation framework for assessing open-weight LLMs on data preparation tasks in longitudinal population studies, where cloud-based models are prohibited by data governance rules. The benchmark uses ground-truth cleaning scripts from six sweeps of a British cohort study, covering tasks like category harmonization and multi-wave merging, with automated evaluation of LLM-generated R code. Current 31–35B parameter open-weight models achieve up to 87.9% average task completion on consumer-grade hardware, suggesting a viable path for AI-assisted data preparation in privacy-sensitive research contexts.