ParaPairAudioBench: Pairwise benchmark reveals large gaps in LALM paralinguistic judgment
Researchers introduce ParaPairAudioBench, a pairwise audio benchmark of 5,175 audio pairs spanning five paralinguistic dimensions (Style, Rate, Emphasis, Age, Gender) designed to evaluate Large Audio-Language Models as judges. Experiments show current LALMs lag human judgment by 32 percentage points on average and exhibit severe calibration failures, especially in ambiguous 'Tie' cases. The benchmark includes same-transcript and cross-transcript conditions to disentangle lexical from acoustic reliance, enabling more rigorous assessment of LALM reliability for speech evaluation.
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BabelJudge: Benchmark for measuring LLM-as-a-judge reliability across languages and agent trajectories
BabelJudge is a new open-source benchmark and audit framework that systematically measures four failure modes in LLM-as-a-judge systems: position bias, verbosity bias, order inconsistency, and cross-lingual degradation. The framework uses a 'gold-labelling by degradation' technique to generate labeled evaluation pairs without human annotation. Evaluation of Qwen2.5-7B-Instruct-4bit across English, Hindi, Arabic, and Swahili reveals severe cross-lingual reliability drops, with Swahili order consistency collapsing to near-random (0.480). The framework is extended to agentic evaluation with nine trajectory-level perturbations and three new metrics, released as a Python package supporting 11 judge backends.
IndicContextEval: Benchmark for context utilisation in Audio LLMs across 8 Indic languages
Researchers introduce IndicContextEval, a 56-hour multilingual speech benchmark covering 555 speakers across 8 Indian languages and 23 professional domains, designed to test whether Audio LLMs genuinely use textual context (domain descriptions, entity lists) or rely on parametric knowledge. The benchmark employs a 7-level prompting framework that progressively introduces contextual signals including adversarial prompts with incorrect entities. Evaluation of five models reveals substantial variation in context utilisation behaviour, exposing a gap in existing ASR benchmarks that test only fixed prompting conditions.
Evaluating Audio Reasoning with Big Bench Audio
Hugging Face introduces Big Bench Audio, a new benchmark designed to evaluate audio reasoning capabilities in AI models. The benchmark appears to extend the Big Bench evaluation framework into the audio domain, targeting multimodal models that process and reason over audio inputs. This release addresses a gap in evaluation tooling for audio-capable language models.
Phun-Bench: A Chinese benchmark for evaluating LLM phonological understanding
Researchers introduce Phun-Bench, a purpose-built benchmark for evaluating LLMs on phonological understanding in Chinese across three dimensions: Homophony, Rhyme, and Phonetic Similarity. The benchmark is designed to avoid rote-memorization shortcuts that plague existing phonological evals. Results show LLMs can recall correct pronunciations but fail to apply phonological knowledge flexibly as human speakers do, and the authors propose a hypothesis about the underlying mechanism of LLM phonological 'perception'.
MMAE: First comprehensive benchmark for instruction-based audio editing across 7 modalities
Researchers introduce MMAE, a 2,000-sample benchmark for evaluating general-purpose instruction-based audio editing systems, covering 7 audio modalities (sound, speech, music, and mixtures) and 6 levels of task complexity. The benchmark uses a rubric-based evaluation framework decomposing tasks into 17,741 verifiable criteria to assess instruction following and context consistency. Evaluation of leading models reveals severe limitations: Exact Match Rate falls below 5% overall and hits 0% on complex mixed-modality tasks, exposing fundamental gaps in current audio editing systems.
FilBench: Benchmarking LLM Capabilities in Filipino Language
FilBench is a new benchmark introduced to evaluate large language models on their ability to understand and generate Filipino. The benchmark targets a historically underrepresented language in NLP evaluation suites, assessing both comprehension and generation tasks. This work addresses gaps in multilingual LLM evaluation coverage, particularly for Southeast Asian languages.
UniAudio-Token: Semantic Speech Tokenizer with General Audio Perception for Audio-LLMs
UniAudio-Token is a framework from Tencent that extends semantic speech tokenizers—commonly used as interfaces for Audio-LLMs—to support general audio perception without sacrificing speech quality. It introduces two mechanisms: Semantic-Acoustic Primitives (SAP) for structured supervision decomposing audio into linguistic, vocal, and auditory-scene components, and Semantic-Acoustic Equilibrium (SAE), a content-aware gating mechanism that restores fine-grained acoustic details from shallow layers. Evaluations show it outperforms all single-codebook baseline tokenizers on both understanding and generation tasks when integrated with downstream LLMs. Code, training/inference scripts, and model checkpoints are publicly released.
Judge Arena: Benchmarking LLMs as Evaluators
Hugging Face and Atla have launched Judge Arena, a platform for benchmarking large language models in their role as automated evaluators. The initiative uses an Elo-based ranking system to compare how well different LLMs judge the quality of model outputs, addressing the growing reliance on LLM-as-judge paradigms in evaluation pipelines. This fills a meta-evaluation gap: as LLM judges become standard practice, understanding their relative reliability and biases becomes critical infrastructure for the field.