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Introduces Instruct-FD, a benchmark for evaluating whether full-duplex speech systems can follow explicit turn-taking instructions. Results show the best model achieves only 64.4% adherence, highlighting a significant gap in instruction-following turn management.
This paper investigates how format, instruction count, and context length affect instruction adherence and hallucination in LLMs through controlled experiments on a synthetic corpus, finding that instruction-following collapses beyond 80 rules regardless of format, and recall degrades sharply after 64-128k tokens with format-dependent effects. It releases the VeyraBench harness for reproduction.
The author recounts issues when using Gemma models to translate reasoning traces, where the model executes instructions in the text instead of translating, highlighting a boundary failure between instruction and payload.
BLOOMZ and mT0 are multilingual models finetuned on the crosslingual task mixture xP3, enabling zero-shot instruction following in dozens of languages.
This paper presents an open-source re-implementation of the NAVER LABS instruction-following pipeline for IWSLT 2026, using SeamlessM4T-v2-large and Qwen3-4B-Instruct, with 100k synthetic examples.
LLM-as-a-Tutor introduces a framework that extends LLM's role from judge to tutor by dynamically adjusting prompt difficulty through pairwise comparison and constraint addition, improving instruction-following performance in reinforcement learning.
MiniCPM5-1B-Claude-Opus-Fable5-Thinking is a compact 1B thinking language model fine-tuned from openbmb/MiniCPM5-1B on Fable 5 data, enhancing coding and instruction-following while retaining the native thinking chat template and tool-call format. It supports up to 128K context and is suitable for local deployment.
This paper proposes SpeechCombine, an instruction-following speech language model trained without any instruction tuning, using only speech pre-training and a weight combination strategy that transfers text LLM capabilities to the speech domain.
This paper describes NAVER LABS Europe's submission to the IWSLT 2026 instruction-following short track, improving upon their previous winning system by using a new speech projector (SpeechMapper) trained solely on ASR data and augmenting training with a synthetic SQA dataset (fakACL). The resulting system ties for first place in the constrained track while using a weaker LLM backbone.
IFStruct, an instruction-following benchmark for structured output by Liquid AI, now has a leaderboard on Hugging Face, aimed at improving small models for local execution and correct tool use.
ifstruct is an instruction-following benchmark for structured output by Liquid AI, designed to push the field toward better small models that can run locally.
Introduces Indi-RomCoM, a benchmark for evaluating LLMs on Romanized Code-Mixed (RCM) instructions in four Indic languages, finding that LLMs underperform on RCM tasks and performance degrades with higher code-mixing density.
Discusses whether AI agents should follow incomplete instructions, exploring safety and alignment considerations.
Seed2.0 is a new model series that addresses complex real-world tasks by improving long-tail knowledge, instruction following, reasoning, visual understanding, and search capabilities. It presents a robust evaluation framework grounded in user needs.
This paper introduces Masked Language Flow Models (MLFMs), which incorporate masking into flow-based language models to enable continuous flow for conditional generation and allow pretrained Masked Diffusion Models to be converted. The authors propose a novel sampler that alternates continuous denoising with discrete unmasking, demonstrating for the first time that flow-based language models can scale to downstream reasoning and instruction-following tasks.
MIT CSAIL researchers developed Masked Inverse Reinforcement Learning (IRL), which uses large language models to clarify ambiguous instructions for robots and focus on key environmental details, reducing the need for extensive demonstration data.
This paper describes FBK's submission to the IWSLT 2026 Instruction Following shared task, developing SpeechLLMs for short-form and long-form speech instruction following, exploring segmentation methods and achieving robust long-form performance with fixed 30-second segmentation.
This paper tackles code generation for no-resource programming languages by building benchmarks and proposing a method that combines further pre-training with weight difference transfer to create specialized instruction-following models at reduced cost.
A finetuned Pythia-6.9B model on two instruction-following datasets for 550 steps becomes capable in 13 languages, showing significant improvement over the base model.
A thought experiment questions whether instructing an AI model to never hallucinate would trigger self-reflection or result in the model gaslighting itself into believing it isn't hallucinating.