Generative retrieval (GR) reformulates information retrieval (IR) by framing it as the generation of document identifiers (docids), thereby enabling an end-to-end optimization and seamless integration with generative language models (LMs). Despite notable progress under supervised training, GR still struggles to generalize to zero-shot IR scenarios, which are prevalent in real-world applications. To tackle this challenge, we propose ZeroGR, a zero-shot generative retrieval framework that leverages natural language instructions to extend GR across a wide range of IR tasks. Specifically, ZeroGR is composed of three key components: (i) an LM-based docid generator that unifies heterogeneous documents (e.g., text, tables, code) into semantically meaningful docids; (ii) an instruction-tuned query generator that generates diverse types of queries from natural language task descriptions to enhance corpus indexing; and (iii) a reverse annealing decoding strategy to balance precision and recall during docid generation. We investigate the impact of instruction fine-tuning scale and find that performance consistently improves as the number of IR tasks encountered during training increases. Empirical results on the BEIR and MAIR benchmarks demonstrate that ZeroGR outperforms strong dense retrieval and generative baselines in zero-shot settings, establishing a new state-of-the-art for instruction-driven GR.

Alignment techniques are critical in ensuring that large language models (LLMs) output helpful and harmless content by enforcing the LLM-generated content to align with human preferences. However, the existence of noisy preferences (NPs), where the responses are mistakenly labelled as chosen or rejected, could spoil the alignment, thus making the LLMs generate useless and even malicious content. Existing methods mitigate the issue of NPs from the loss perspective by adjusting the alignment loss based on a clean validation dataset. Orthogonal to these loss-oriented methods, we propose perplexity-aware correction (PerpCorrect) from the data perspective for robust alignment which detects and corrects NPs based on the differences between the perplexity of the chosen and rejected responses (dubbed as PPLDiff). Intuitively, a higher PPLDiff indicates a higher probability of the NP because a rejected/chosen response which is mistakenly labelled as chosen/rejected is less preferable to be generated by an aligned LLM, thus having a higher/lower perplexity. PerpCorrect works in three steps: (1) PerpCorrect aligns a surrogate LLM using the clean validation data to make the PPLDiff able to distinguish clean preferences (CPs) and NPs. (2) PerpCorrect further aligns the surrogate LLM by incorporating the reliable clean training data whose PPLDiff is extremely small and reliable noisy training data whose PPLDiff is extremely large after correction to boost the discriminatory power. (3) Detecting and correcting NPs according to the PPLDiff obtained by the aligned surrogate LLM to obtain a denoised training dataset for robust alignment. Comprehensive experiments validate that our proposed PerpCorrect can achieve state-of-the-art alignment performance under NPs. Notably, PerpCorrect demonstrates practical utility by requiring only a modest amount of validation data and being compatible with various alignment techniques.

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM’s adversarial robustness. This paper proposes an efficient tool to audit the LLM’s adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions. Our source code is available at https://github.com/GodXuxilie/PromptAttack.