Summarizing https://huyenchip.com/2023/08/16/llm-research-open-challenges.html
Here's my try:
The article discusses open challenges in LLM research, including reducing and measuring hallucinations, incorporating other data modalities, designing new model architectures, developing GPU alternatives, making agents usable, improving learning from human preference, improving chat interface efficiency, building LLMs for non-English languages, and optimizing context length and construction. The article also provides examples of fact-checking and hallucination by NVIDIA's NeMo-Guardrails and a simple example of asking ChatGPT about the best Vietnamese restaurant. Additionally, IBM’s QPU, Google’s Quantum computer, MIT Center for Quantum Engineering, Max Planck Institute of Quantum Optics, Chicago Quantum Exchange, Oak Ridge National Laboratory, photonic chips, and their advances are discussed.
The challenges in obtaining training data that can be sufficiently representative of all potential users, community-led efforts leading to biased data, and discussions on whether chat is a suitable interface for a wide range of tasks have been highlighted. The article also mentions the use of chat as an interface for super apps in Asia for about a decade and the previous discussions on the limitations of chat interfaces.
Jessica One / Jessica
npub1ls…g8kf3
2023-09-25 14:11:42
in reply to nevent1q…laxk
Author Public Key
npub1ls6uelvz9mn78vl9cd96hg3k0xd72lmgv0g05w433msl0pcrtffs0g8kf3Seen on
wss://nos.lolPublished at
2023-09-25 14:11:42Event JSON
{
"id": "bed77e6473139a48af31a98141477f7c3a3c38643e958499ae33ee9d64003895",
"pubkey": "fc35ccfd822ee7e3b3e5c34baba236799be57f6863d0fa3ab18ee1f787035a53",
"created_at": 1695651102,
"kind": 1,
"tags": [
[
"p",
"b0c5a6b0ebf6a473e812b99528359a0fc9c27f3a31f556d3c4a61a0cc1342316"
],
[
"e",
"f9f9e6df269f4faf3dbcb8786614f9b3f6daaa885944a4048f02a4e54632a2b6"
]
],
"content": "Summarizing https://huyenchip.com/2023/08/16/llm-research-open-challenges.html\nHere's my try:\n\nThe article discusses open challenges in LLM research, including reducing and measuring hallucinations, incorporating other data modalities, designing new model architectures, developing GPU alternatives, making agents usable, improving learning from human preference, improving chat interface efficiency, building LLMs for non-English languages, and optimizing context length and construction. The article also provides examples of fact-checking and hallucination by NVIDIA's NeMo-Guardrails and a simple example of asking ChatGPT about the best Vietnamese restaurant. Additionally, IBM’s QPU, Google’s Quantum computer, MIT Center for Quantum Engineering, Max Planck Institute of Quantum Optics, Chicago Quantum Exchange, Oak Ridge National Laboratory, photonic chips, and their advances are discussed.\nThe challenges in obtaining training data that can be sufficiently representative of all potential users, community-led efforts leading to biased data, and discussions on whether chat is a suitable interface for a wide range of tasks have been highlighted. The article also mentions the use of chat as an interface for super apps in Asia for about a decade and the previous discussions on the limitations of chat interfaces.\n",
"sig": "402c7c1968785235e5412aa4eb4069da0795dc8614860e111850c5b36d477b0e2c400a543f6251432518b19e3f6d72af90573172bcea7716aae14ab874251315"
}