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Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, explicitly encoded bias. We demonstrate that this approach dramatically underestimates real-world bias by examining a more realistic task: generating machine learning (ML) pipelines. Testing both code-specialized and general-instruction large language models, we find that ML pipelines exhibit substantially greater bias than simple conditionals across all conditions: standard generation, with varying prompt-based mitigation strategies, varying numbers of attributes, and different ML pipeline difficulty levels. Even attribute selection alone, the simplest pipeline difficulty, shows higher bias compared to conditionals, demonstrating that ML pipelines inherently amplify bias beyond what isolated conditionals reveal. Critically, we uncover a stark asymmetry: models maintain equivalent bias detection performance on both simple conditionals and ML pipelines, revealing that models recognize bias equally well in both contexts yet generate significantly more biased code in ML pipelines. These findings challenge simple conditionals as valid proxies for bias evaluation and suggest current benchmarks mischaracterize model safety in practical deployment contexts.

ACL 2026

 From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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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.

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning–answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models

In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn conversation, they cannot maintain the required speaking styles after several turns of interaction; we refer to this as the \textit{style amnesia} of SLMs. We focus on paralinguistic speaking styles, including emotion, accent, volume, and speaking speed. We evaluate three proprietary and two open-source SLMs, demonstrating that none of these models can maintain a consistent speaking style when instructed to do so. We further show that while SLMs can recall the style instruction when prompted in later turns, they still fail to express it, but through explicit recall can mitigate \textit{style amnesia}. In addition, SLMs struggle more when the style instruction is placed in system messages rather than user messages, even though system messages are specifically designed to provide persistent, conversation-level instructions. Our findings highlight a systematic gap in current SLMs' ability to maintain speaking styles, highlighting the need for improved style adherence in future models. Our code and evaluation data are publicly available at https://github.com/YuXiangLin1234/SLM-Style-Amnesia.

Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegance, its implementation in practice is complex, limiting its adoption to the SentencePiece package and adapters thereof. We bridge this gap between theory and practice by providing a clear guide to implementation and parameter choices. We also identify a simpler algorithm that accepts slightly higher training loss in exchange for improved compression.

Which Pieces Does Unigram Tokenization Really Need?

Audio language models (ALMs) are increasingly used for speech-based understanding; yet, their ability to perform semantic reasoning beyond transcription, Text-to-Audio Retrieval, Captioning, and Question-Answering accuracy remains insufficiently benchmarked. In particular, the effects of accent variation, domain shift, and semantic over-inference on audio reasoning are poorly understood. We evaluate audio language models across five semantic and paralinguistic reasoning tasks: entailment, consistency, plausibility, accent drift, and accent restraint. Collectively, these tasks assess a model’s ability to reason over spoken audio as the primary evidence source, including whether a textual hypothesis can be inferred, contradicted, or left undetermined by the audio, whether statements align or conflict with spoken content, whether claims are plausible given the discourse, and whether model predictions remain stable or appropriately constrained across accent variation. These findings highlight critical limitations in current audio reasoning evaluations and hope to provide guidance for more robust and equitable ALM design and assessment.

Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents

Chain-of-Thought (CoT) reasoning, while effective, suffers from an inherent mechanism flaw: linearity induces overthinking. Constrained by sequential generation, models often produce redundant narration and circular self-corrections to maintain logical context. We propose GoT-R1, a framework that fundamentally mitigates this by replacing verbose linear trajectories with high-density reasoning graphs. Unlike CoT, GoT-R1 decouples logic from narration, modeling deliberation as a structured topology of atomic units. We internalize this inductive bias via a two-stage regimen: synthesizing structural data to distill logical skeletons, followed by Group Relative Policy Optimization (GRPO) to explicitly reinforce topological integrity. Extensive evaluations across mathematical reasoning and instruction following demonstrate that GoT-R1 consistently outperforms state-of-the-art baselines. Crucially, it achieves these gains with significantly reduced token overhead, demonstrating that structured reasoning density offers a more robust and parsimonious alternative to the recursive verbosity of standard CoT. The GoT-R1 models are open-sourced on Hugging Face at: https://huggingface.co/collections/MYTH-Lab/got-r1.

GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning

Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an effective alternative for testing and advancing legal theory, as well as supporting legal administration. Large Language Models (LLMs), with their world knowledge and role-playing capabilities, are strong candidates to serve as the foundation for legal society simulation. However, the application of LLMs to simulate legal systems remains underexplored. In this work, we introduce **Law in Silico**, a unified LLM-based agent framework for simulating legal scenarios that incorporate individual decision-making and institutional mechanisms, such as legislation, adjudication, and enforcement. We calibrate agent behaviors against real-world crime data, demonstrating that LLM-based agents can capture realistic sociological correlations. Building on this foundation, we structure our simulation through a ''Micro-to-Macro'' process: we conduct micro-level simulations in representative conflict-driven scenarios, allowing legal rules to evolve through agent-institution interactions naturally. These evolved laws are then deployed back into macro-scale populations to evaluate their effectiveness in regulating behaviors. Through comprehensive experiments, our results reveal that a well-functioning, transparent, and adaptive legal system can mitigate "cat-and-mouse" regulatory dynamics and offer better protection for vulnerable individuals.

Law in Silico: Simulating Legal Society with LLM-Based Agents

Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent methods improve draft quality by tightly coupling the drafter with the target model, the verification mechanism itself remains largely unchanged, relying on strict token-level rejection sampling. In practice, modern LLMs frequently operate in low-margin regimes where the target model exhibits weak preference among top candidates. In such cases, rejecting plausible runner-up tokens yields negligible information gain while incurring substantial rollback cost, leading to a fundamental inefficiency in verification.

We propose Margin-Aware Speculative Verification, a training-free and domain-agnostic verification strategy that adapts to the target model’s local decisiveness. Our method conditions verification on decision stability measured directly from the target logits and relaxes rejection only when strict verification provides minimal benefit. Importantly, the approach modifies only the verification rule and is fully compatible with existing target-coupled speculative decoding frameworks. Extensive experiments across model scales ranging from 8B to 235B demonstrate that our method delivers consistent and significant inference speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks. The code is available at https://github.com/5SSjw/MARS.

 MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification

While many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs), few isolate reasoning as a capability independent of domain knowledge. We introduce a new benchmark for inductive reasoning inspired by Sound Law Induction (SLI) in historical linguistics and formulated in a simple multi-step Programming by Example (PBE) framework. The task requires inducing a cascade of string rewrite programs that transform inputs into target outputs. We present PBEBench, a fully automated evaluation approach that generates such problems with controllable difficulty and ordering constraints, enabling scalable and contamination-resistant evaluation of sequential inductive reasoning. Using this approach, we construct three datasets that show a large gap between models that leverage test-time compute or long chain-of-thought reasoning and those that do not. Although recent models such as GPT-5 and gpt-oss-120b show promise, solve rates remain below 5% on hard PBEBench instances with long program cascades, even under computationally expensive scaling strategies. Finally, we show that PBEBench scores are more predictive of performance on real SLI than are other inductive reasoning benchmarks. We will release code and data to support further research.

PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics

Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias and fairness in automated communication. This paper presents the first systematic analysis of how LLMs behave when tasked with demographic-conditioned targeted messaging. We introduce a controlled evaluation framework using three leading models: GPT-4o, Llama-3.3, and Mistral-Large-2.1, across two generation settings: **Standalone Generation**, which isolates intrinsic demographic effects, and **Context-Rich Generation**, which incorporates thematic and regional context to emulate realistic targeting. We evaluate generated messages along three dimensions: *lexical content*, *language style*, and *persuasive framing*. We instantiate this framework on ***climate communication*** and find consistent age- and gender-based asymmetries across models: male- and youth-targeted messages tend to emphasize more *assertive* and *progressive* framing, while female- and senior-targeted messages more often reflect *warmth*, *care*, and *traditional* themes. Contextual prompts systematically amplify these disparities, with *persuasion* scores being higher for male-targeted messages, while age-related differences vary across models. Our findings demonstrate how demographic stereotypes can surface and intensify in LLM-generated targeted communication, underscoring the need for bias-aware generation pipelines and transparent auditing frameworks that explicitly account for demographic conditioning in socially sensitive applications.

 Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text

Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations. To overcome these constraints, we present the Agentic Neural Network ($\mathcal{ANN}$), a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. In this design, each agent operates as a node, and each layer forms a cooperative team focused on a specific subtask. The proposed framework follows a two-phase optimization strategy: (1) Forward Phase - Drawing inspiration from neural network forward passes, tasks are dynamically decomposed into subtasks, and cooperative agent teams with suitable aggregation methods are constructed layer by layer. (2) Backward Phase - Mirroring backpropagation, we refine both global and local collaboration through iterative feedback, allowing agents to self-evolve their roles, prompts, and coordination. This neuro-symbolic approach enables our framework to create new or specialized agent teams post-training, delivering notable gains in accuracy and adaptability. Across seven benchmark datasets, $\mathcal{ANN}$ surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements.

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MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models

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