Summary of With Greater Text Comes Greater Necessity: Inference-time Training Helps Long Text Generation, by Y. Wang et al.
With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation
by Y. Wang, D. Ma, D. Cai
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Long text generation, such as novel writing and discourse-level translation with extremely long contexts, poses significant challenges to current language models. Temp-Lora, an alternative approach, embeds context information directly into a temporary Lora module, progressively training it during the generation process. This method efficiently preserves contextual knowledge while preventing permanent alterations to the model’s parameters. Extensive experiments on PG19 and GuoFeng benchmarks validate Temp-Lora’s effectiveness in enhancing long text generation quality, reducing computational costs, and improving memory usage and latency for inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: A new way of writing long texts, like novels or articles, is being developed. This method helps language models keep track of a lot of information without making the model change too much. The new approach uses a special module that learns from the text it generates. This makes it better at writing long texts and uses less computer power than other methods. |
Keywords
» Artificial intelligence » Discourse » Inference » Lora » Text generation » Translation
