Generative language models (GLMs) excel at text generation and instruction following, while discriminative language models (DLMs) provide strong semantic representations for understanding tasks. Under the manifold hypothesis, natural language semantics can be regarded as lying on a shared latent semantic manifold, while DLMs and GLMs learn different representations of this manifold in their respective latent spaces. However, due to differences in training objectives and modeling strategies, these representation spaces are not naturally aligned, preventing discriminative representations from being directly utilized by GLMs through simple concatenation or linear transformation. To address this issue, a semantic alignment framework through discriminative conditioning for generation (DisCon4Gen) is proposed. From a manifold-based perspective, DisCon4Gen transforms DLM semantics into continuous conditions for GLMs. It further incorporates both global semantic signals and fine-grained discriminative cues to guide generation, enabling GLMs to better exploit external discriminative semantics. Experiments on five downstream datasets show that DisCon4Gen improves over strong supervised fine-tuning baselines and achieves competitive performance across both semantic understanding and generative reasoning tasks.

Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation scenarios, often lacking assessments in real multi-turn dialogue contexts; (2) narrow evaluation dimensions, with insufficient detailed assessments of how LLMs use tools; and (3) reliance on LLMs or real API executions for evaluation, which introduces significant overhead. To address these challenges, we introduce ACEBench, a comprehensive benchmark for assessing tool usage in LLMs. ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. “Normal” evaluates tool usage in basic scenarios; “Special” evaluates tool usage in situations with ambiguous or incomplete instructions; “Agent” evaluates tool usage through multi-agent interactions to simulate real-world, multi-turn dialogues. We conducted extensive experiments using ACEBench, analyzing various LLMs in-depth and providing a more granular examination of error causes across different data types.