Taw qhia
Cov Qauv Lus Loj (LLMs) tau hloov pauv lub teb ntawm kev ua cov lus ntuj (NLP) hauv xyoo tas los no. Cov qauv no tau ua tiav cov txiaj ntsig ntawm lub xeev ntawm ntau yam haujlwm NLP, suav nrog kev txhais lus, sau ntawv sau, thiab teb cov lus nug. Txawm li cas los xij, qhov tseeb muaj peev xwm ntawm LLMs nyob hauv lawv lub peev xwm los xav thiab nkag siab txog kev sib raug zoo ntawm cov ntsiab lus. Hauv tsab xov xwm no, peb yuav piav qhia txog kev nce qib hauv LLM qhov laj thawj, tshawb txog cov txheej txheem hauv qab, cov qauv tsim, thiab cov teeb meem.
keeb kwm
LLMs yog ib hom neural network uas tau kawm txog ntau cov ntaub ntawv hauv cov ntawv nyeem kom kawm cov qauv thiab kev sib raug zoo ntawm cov lus thiab cov ntsiab lus. Cov qauv no feem ntau muaj xws li tus encoder thiab tus decoder, qhov twg tus encoder ua cov ntawv sau thiab tus decoder tsim cov ntawv tso tawm. Tus encoder feem ntau yog ib qho transformer-based architecture, uas yog qhov tshwj xeeb zoo rau cov ntaub ntawv raws li cov ntawv nyeem.
Transformer Architecture
Lub transformer architecture, qhia nyob rau hauv daim ntawv "Tsom ntsoov yog txhua yam koj xav tau" los ntawm Vaswani li al. (2017), tau dhau los ua tus qauv de facto rau LLMs. Lub transformer muaj ib tug encoder thiab ib tug decoder, txhua tus muaj ntau txheej txheej. Txhua txheej muaj ob txheej txheej: ib qho kev mloog tus kheej thiab kev sib txuas nrog kev sib txuas ua ke (FFNN).
`` python import taws import torch.nn as nn
chav TransformerLayer(nn.Module): def init(self, d_model, n_heads, d_ff): super(TransformerLayer, tus kheej).init() self.self_attn = nn.MultiHeadAttention(d_model, n_heads) self.ffnn = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(0.1) self.layer_norm = nn.LayerNorm(d_model)
def forward(tus kheej, x):
# Kev mloog tus kheej mechanism
x_attn = self.self_attn(x, x)
x_attn = self.dropout(x_attn)
x_attn = x + x_attn
#FFNN
x_ffnn = self.ffnn(x_attn)
x_ffnn = self.dropout(x_ffnn)
x_ffnn = self.layer_norm(x_ffnn + x_attn)
rov x_ffnn
## Kev nce qib hauv LLM Reasoning
LLMs tau ua tiav qhov tseem ceeb hauv kev xav thiab nkag siab txog kev sib raug zoo ntawm cov ntsiab lus. Qee qhov kev nce qib tseem ceeb suav nrog:
### 1. Daim duab-raws li kev xav
Graph-based reasoning suav nrog kev sawv cev kev paub raws li daim duab, qhov twg cov nodes sawv cev rau cov ntsiab lus thiab cov npoo sawv cev rau kev sib raug zoo ntawm lawv. LLMs tuaj yeem raug cob qhia rau kev xav txog cov duab no, ua kev kwv yees txog kev sib raug zoo ntawm cov nodes.
``` xeb
siv graph::Graph;
siv neural_network::NeuralNetwork;
fn graph_reasoning(graph: &Graph, lus nug: &str) -> String {
cia embedding = neural_network.query(graph, query);
cia result = neural_network.reason(graph, embedding);
result.to_string()
}
2. Kev Kawm Ntau Yam
Kev kawm ntau yam kev kawm suav nrog kev cob qhia ib tus qauv ntawm ntau yam haujlwm ib txhij. Txoj hauv kev no tau pom los txhim kho kev ua haujlwm ntawm LLMs ntawm ntau yam dej num, suav nrog kev xav thiab nkag siab txog kev sib raug zoo.
`` mus pob lub ntsiab
import ( "fmt" "gonum.org/v1/gonum/mat" )
hom qauv struct { * nn. Kab * nn. Kab }
func (m *Model) Forward(x *mat.Dense) *mat.Dense { // Forward pass z := m.Linear(x) z = m.Linear(z) rov z }
### 3. Ceev Mechanisms
Cov txheej txheem saib xyuas tso cai rau LLMs tsom mus rau qhov tshwj xeeb ntawm cov ntawv sau tawm thaum tsim cov zis. Txoj hauv kev no tau pom los txhim kho kev ua haujlwm ntawm LLMs ntawm ntau yam haujlwm, suav nrog kev xav thiab nkag siab txog kev sib raug zoo.
`` python
import taws
import torch.nn as nn
Chav Kawm AttentionMechanism(nn.Module):
def __init__(self, d_model, n_heads):
super(AttentionMechanism, tus kheej).__init__()
self.query = nn.Linear(d_model, d_model)
tus kheej.key = nn.Linear(d_model, d_model)
tus kheej.value = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(0.1)
self.layer_norm = nn.LayerNorm(d_model)
def forward(tus kheej, x):
# Cov lus nug, qhov tseem ceeb, thiab tus nqi
q = self.query(x)
k = self.key(x)
v = self.value(x)
# Ceev faj qhov hnyav
luj = torch.matmul(q, k.T) / math.sqrt(q.shape[1])
weights = self.dropout(weights)
luj = torch.softmax(yuav, dim=-1)
# Cov ntsiab lus vector
context = torch.matmul(tws, v)
rov qab cov ntsiab lus
Visualization
Txhawm rau pom qhov ua tau zoo ntawm LLMs ntawm ntau yam haujlwm, peb tuaj yeem siv daim ntawv qhia los sib piv qhov tseeb ntawm cov qauv sib txawv ntawm ib txoj haujlwm. Nov yog ib qho piv txwv ntawm daim duab siv Chart.js:
``json { "type": "bar", "data": { "labels": ["Task 1", "Task 2", "Task 3"], "datasets": [ { "label": "Model 1", "cov ntaub ntawv": [0.8, 0.7, 0.9], "backgroundColor": "rgba(255, 99, 132, 0.2)", "borderColor": "rgba(255, 99, 132, 1)", "borderWidth": 1 }, { "label": "Model 2", "cov ntaub ntawv": [0.9, 0.8, 0.7], "backgroundColor": "rgba(54, 162, 235, 0.2)", "borderColor": "rgba(54, 162, 235, 1)", "borderWidth": 1 } ] }, "options": { "title": { "display": tseeb, "text": "Qhov tseeb ntawm cov qauv sib txawv ntawm ntau yam haujlwm" }, "scales": { "yAxes": [ { "ticks": { "beginAtZero": tseeb } } ] } } }
## Xaus
Hauv tsab xov xwm no, peb tau tshawb nrhiav qhov kev nce qib hauv LLM qhov laj thawj, suav nrog kev ua haujlwm raws li kev xav, kev kawm ntau yam, thiab cov txheej txheem saib xyuas. Peb kuj tau muab cov qauv piv txwv hauv Python, Rust, thiab Go los qhia txog cov ntsiab lus no. Thaum kawg, peb tau siv daim ntawv qhia kom pom qhov ua tau zoo ntawm LLMs ntawm ntau yam haujlwm. Cov kev nce qib no muaj peev xwm los hloov pauv kev ua haujlwm ntawm NLP, ua rau LLMs xav txog thiab nkag siab txog kev sib raug zoo ntawm cov ntsiab lus.
## Cov ntaub ntawv
* Vaswani et al. (2017). Nco ntsoov yog txhua yam koj xav tau. arXiv preprint arXiv:1706.03762.
* Devlin et al. (2018). BERT: Kev cob qhia ua ntej ntawm Deep Bidirectional Transformers rau Kev Nkag Siab. arXiv preprint arXiv:1810.04805.
* Radford et al. (2019). Cov qauv lus yog Unsupervised Multitask Learners. arXiv preprint arXiv:1906.08237.