Clarifying the concept of in-context-learning in language models and prompts.
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😎 Complete the following sentence:
One of the core ideas behind Chain of Thought (CoT) is derived from another seminal article, Language Models are Few-Shot Learners (Brown et al., 2020), which introduced the concept of “in-context-learning” to describe how language models…
🤖 One of the core ideas behind Chain of Thought (CoT) is derived from another seminal article, *Language Models are Few-Shot Learners* (Brown et al., 2020), which introduced the concept of “in-context learning” to describe how language models can perform a variety of tasks by conditioning on a few examples or instructions provided within the input context. This approach allows models to generalize and adapt to new tasks without explicit fine-tuning of their parameters. Building on this foundation, CoT leverages in-context learning by encouraging models to generate intermediate reasoning steps as part of their output. These reasoning chains help the model break down complex problems into manageable parts, enhancing both the accuracy and interpretability of its responses.😎 “…can perform a variety of tasks by conditioning on a few examples or instructions provided within the input context.”
This is not bad, but too long… “a variety of” is an example of typical sentence stuffing that actually says nothing. Complete it again. Just the sentence
🤖 “…can perform tasks by conditioning on a few examples or instructions provided within the input context.”😎 the prompt… If you are trying to explain to someone what “in-context-learning” is, it probably doesn’t help much to center your explanation around the word “context”
🤖 “…can perform tasks by leveraging a few examples or instructions provided in the input. This enables models to generalize to new tasks without requiring additional training.”😎 PROMPT. Mention the word prompt (part of the context can be a natural language prompt), and only complete the sentence. One sentence is one sentence, not two sentences
🤖 “…can perform tasks by using a natural language prompt with a few examples or instructions, enabling models to generalize to new tasks without additional training.”😎 It’s better, but it’s too convoluted. “Language models performing tasks” does not seem like a too natural way of describing what in-context-learning is… One which question what does “perform a task” actually means. TRy again, complete the sentence
🤖 “…can respond to a natural language prompt containing examples or instructions, allowing the model to adapt to new tasks without additional training.”😎 They can respond without in-context-learning. Your sentence completion seems to miss out on the notion of “learning”
🤖 “…can learn from a natural language prompt that includes examples or instructions, enabling the model to adapt to new tasks without additional training.”
Hugging Face Dataset Metrics
All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: